INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026
Page 642
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Study and Design of AI-Driven Models for Enhancing Search Engine
Visibility and Website Performance Optimization: A Survey-Based
DOI:
https://doi.org/10.51583/IJLTEMAS.2026.15020000057
Received: 05 February 2026; Accepted: 11 February 2026; Published: 13 March 2026
ABSTRACT
This research focuses on the study and design of Artificial Intelligence (AI)-driven models for enhancing search
engine visibility and optimizing website performance. The study adopts a survey-based quantitative approach,
supported by an extensive literature review, to analyze user behavior, website performance expectations, and
awareness of AI applications in Search Engine Optimization (SEO). Data collected from more than 1250
respondents reveal that website loading speed, navigation, and mobile friendliness are the most influential
factors affecting user experience and search rankings.
The findings indicate that AI-based techniques such as automated keyword analysis, semantic content
optimization, learning-to-rank models, and predictive performance analytics can significantly improve SEO
outcomes and website efficiency. Based on empirical insights and literature synthesis, this study proposes an AI-
driven integrated framework combining machine learning, natural language processing, and performance
monitoring to enhance search visibility and user engagement. The research contributes by bridging the gap
between AI-based SEO strategies and website performance optimization within a unified model.
INTRODUCTION
Search Engine Optimization (SEO) has become a critical component of digital presence in an increasingly
competitive online ecosystem. Search engines rely on complex algorithms to rank web pages based on relevance,
authority, and user experience. The introduction of Artificial Intelligence (AI) has fundamentally transformed
how search engines interpret queries, evaluate content, and deliver results.
AI-based systems such as Google RankBrain, BERT, and DeepRank enable search engines to better understand
search intent, semantics, and contextual relevance. At the same time, website performance metrics, including
Core Web Vitals (Largest Contentful Paint, First Input Delay, and Cumulative Layout Shift), loading speed, and
mobile responsiveness, have become direct ranking factors.
Despite significant advancements, most existing research focuses either on AI-based ranking mechanisms or
website performance optimization as independent domains. There is limited empirical research integrating
AIdriven SEO strategies, website performance optimization, and user perception within a single framework.
This study addresses this research gap by designing a survey-based empirical investigation and proposing an AI-
driven model for enhancing both search engine visibility and website performance.
LITERATURE REVIEW
Early search engine ranking models such as PageRank relied primarily on hyperlink structures to measure page
authority. Subsequent advancements introduced Learning-to-Rank (LTR) algorithms such as RankNet,
LambdaRank, and LambdaMART, which used supervised machine learning to improve relevance prediction.
125001
Approach
Man Mohan Singla, Prof (Dr.) Shailesh Kumar
Dean Department of CSE School of Engineering & Technology, Om Sterling Global University Hisar,
Ph.D. Scholar (Student) at CSE School of Engineering & Technology, Om Sterling Global University Hisar,
125001
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026
Page 643
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With the evolution of deep learning, Transformer-based architectures such as BERT significantly improved
contextual understanding of search queries and content. Research by Devlin et al. demonstrated how bidirectional
transformers enhance semantic interpretation in search systems.
Recent studies emphasize the role of AI in website performance optimization, including predictive caching,
load balancing, dynamic resource allocation, and performance monitoring using machine learning techniques.
Mathur et al. (2024) demonstrated the effectiveness of predictive analytics in optimizing website performance
metrics.
Further research highlights AI-driven automation in SEO practices such as keyword prediction, content
optimization, personalization, voice-search readiness, and Answer Engine Optimization (AEO). However,
existing studies largely treat SEO automation and performance optimization separately, creating a gap that this
research seeks to address through an integrated AI-driven approach supported by user-centric data.
METHODOLOGY
This study follows a quantitative survey-based research methodology to capture user perceptions and
experiences related to search engines, website performance, and AI-driven SEO.
Data Collection
Tool Used: Google Forms
Sample Size: 1250+ respondents
Respondent Categories: Students, professionals, and business owners
Data Processing and Analysis
Data Cleaning: Microsoft Excel
Visualization: Python
Analysis Techniques: Frequency distribution and thematic categorization
Survey Structure
The survey consisted of 15 structured questions covering:
Demographic information
Search engine usage patterns
Website performance experience
Awareness and perception of AI in SEO
RESULTS AND SURVEY ANALYSIS
Demographic Overview
Table 1: Age Distribution of Respondents
Age Group
Respondents
Percentage
Below 18
66
5.3%
18–30
808
64.7%
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026
Page 644
www.rsisinternational.org
31–45
275
22%
46+
100
8%
The majority of respondents belong to the 18–30 age group, indicating strong participation from students and
early-career professionals.
Search Engine Preferences
Table 2: Preferred Search Engine
Search Engine
Respondents
Google
1150
Bing
59
Yahoo
25
Others
16
Google clearly dominates search engine usage across all demographic groups.
Website Performance Experience
Table 3: Key Factors Affecting Website Experience
Factor
Importance (%)
Loading Speed
86%
Navigation
78%
Design
74%
Content Quality
68%
Mobile Friendliness
64%
Website loading speed and navigation emerged as the most critical factors influencing user satisfaction and
retention.
Awareness and Perception of AI in SEO
Table 4: Awareness of AI in SEO
Awareness Level
Respondents
Percentage
High
209
16.7%
Moderate
512
41.3%
Low
525
42%
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026
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Table 5: Perception of AI Improving SEO
Response
Respondents
Percentage
Strongly Agree
609
48.7%
Agree
409
32.7%
Neutral
150
12%
Disagree
82
6.6%
Over 81% of respondents believe that AI can significantly improve search rankings and website performance.
DISCUSSION
The results confirm that user experience factors, particularly website speed and navigation, play a decisive role
in influencing search engine trust and rankings. These findings align with prior research emphasizing the
importance of Core Web Vitals and performance optimization in SEO.
The strong user belief in AI-driven SEO improvements supports existing literature on automated keyword
analysis, semantic optimization, and predictive performance monitoring. By integrating AI techniques across
SEO and performance domains, organizations can achieve sustainable improvements in visibility and
engagement.
Proposed AI-Driven Model
Figure: AI-Driven Framework for Search Visibility and Performance Optimization
Model Components:
Data Input Layer: Website analytics, keyword trends, user behavior data
AI Processing Layer: NLP for content analysis, ML models for ranking prediction
Optimization Engine: Automated SEO adjustments, speed optimization, layout enhancement
Performance Monitoring Layer: Core Web Vitals tracking and ranking analysis
Feedback Loop: Continuous learning and adaptive optimization
The proposed model is conceptual and intended for future prototype development and empirical validation.
CONCLUSION AND FUTURE WORK
This study demonstrates that AI-driven approaches can significantly enhance search engine visibility and website
performance by automating SEO processes and improving user experience. The survey-based findings validate
the relevance of AI in modern SEO practices.
Future work will focus on implementing the proposed framework as a functional prototype and evaluating its
effectiveness across different industries and web environments.
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