This research explores the integration of deep learning (DL) techniques to create personalized learning pathways in higher education, aiming to bridge the gap between static educational content and dynamic student needs. The study implemented an AI-driven adaptive learning platform across four courses and 300 students at a university in Faisalabad, Pakistan. A controlled experiment compared student outcomes between those using the AI platform and those receiving traditional instruction. Quantitative results showed a 25% improvement in grades, test scores, and engagement for the AI group, with a statistically significant p-value of 0.00045. Qualitative feedback highlighted enhanced experiences attributed to personalized pathways. The DL analysis of student performance data revealed key parameters, including improved learning outcomes and engagement metrics over time. Surveys indicated increased satisfaction compared to traditional methods. The research provides a concrete framework for institutions to implement personalized, AI-driven education at scale, contributing to the growing demand for AI in education research and offering practical insights for more adaptive and student-centric teaching methodologies.This research explores the integration of deep learning (DL) techniques to create personalized learning pathways in higher education, aiming to bridge the gap between static educational content and dynamic student needs. The study implemented an AI-driven adaptive learning platform across four courses and 300 students at a university in Faisalabad, Pakistan. A controlled experiment compared student outcomes between those using the AI platform and those receiving traditional instruction. Quantitative results showed a 25% improvement in grades, test scores, and engagement for the AI group, with a statistically significant p-value of 0.00045. Qualitative feedback highlighted enhanced experiences attributed to personalized pathways. The DL analysis of student performance data revealed key parameters, including improved learning outcomes and engagement metrics over time. Surveys indicated increased satisfaction compared to traditional methods. The research provides a concrete framework for institutions to implement personalized, AI-driven education at scale, contributing to the growing demand for AI in education research and offering practical insights for more adaptive and student-centric teaching methodologies.