This study examines the relationship between Artificial Intelligence (AI) adoption and the financial performance of Nigeria’s top-tier deposit money banks over a ten year period (2015–2024). Against the backdrop of Nigeria’s accelerating digital transformation, the research examines how AI-driven innovations enhance profitability within the banking sector. The analysis focuses on five Tier-1 institutions namely; First Bank, UBA, GTBank, Access Bank, and Zenith Bank collectively known as the FUGAZ banks. Financial performance is measured using Return on Assets (ROA), while AI adoption is proxied through three major applications: Automated Chatbot Systems (ACBS), Deep Learning Models in credit risk management (DLM), and I-Comply fraud detection systems (I-COMP). Data were obtained from the audited annual reports of the five listed banks and validated through a supplementary survey of bank IT and operations managers. Each AI variable was operationalized as a binary indicator (1 = adopted by the bank in a given year; 0 = otherwise) and aggregated into a balanced panel dataset. Data were tested for stationarity and cointegration using Levin–Lin–Chu and Pedroni tests. Based on the confirmation of long-run equilibrium relationships, a Panel Fully Modified Ordinary Least Squares (FM-OLS) regression was employed, supported by robustness checks using fixed-effects estimation. The results reveal that ACBS, DLM, and I-COMP each have a positive and statistically significant association with ROA (p < 0.05), with an overall model explanatory power (R²) of 0.71. The Durbin–Watson statistic of 1.82 indicates no serial correlation, and diagnostic tests confirm model adequacy. The findings imply that AI adoption significantly enhances profitability through improved customer interaction, efficient credit assessment, and proactive fraud prevention. The study contributes to empirical literature on digital transformation in emerging markets and recommends that Nigerian banks deepen their AI integration strategies to strengthen competitiveness and shareholder value in an increasingly data-driven economy.