(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
The explosion of electronic communication has made email a foundation of contemporary professional and educational existence. Nevertheless, the large amount of unstructured correspondence places an enormous cognitive burden on the users, resulting in ineffective task processing, lost deadlines, and lowered productivity. This paper presents an intelligent personal assistant aimed at addressing these issues. The system combines a cutting-edge large language model (LLM) with general productivity APIs, such as Gmail and Google Calendar, to develop a seamless, automated process. The system feeds on and parses email content automatically to carry out three fundamental functions: creating brief summaries for rapid understanding, extracting action items and deadlines to schedule matching events in an electronic calendar, and offering a question-answering interface where users can pose particular questions about an email's content. A strict experimental analysis carried out on a manually annotated subset of the Enron email corpus proves the effectiveness of the system. The system attained an F1-score of 0.87 for extracting tasks and deadlines and ROUGE-L scores of 0.42 for summarization, reflecting high-quality performance. Qualitative analysis also supports the question-answering module's capability to correctly retrieve information. The main contribution of this project is the conception, implementation, and evaluation of a strong, end-to-end system that adequately closes the semantic gap between passive email information and actionable, queryable intelligence, offering an effective solution for individual digital productivity enhancement.