This work aims to revolutionize educational management by leveraging cutting-edge technologies to address the unique challenges faced by individuals in managing academic pursuits, while dealing with their diverse other commitments. This paper focuses on utilizing genetic learning algorithms to dynamically generate optimized study schedules based on individual profiles, promoting efficient time management and improving learning outcomes. The primary objective is to develop an intelligent and adaptive study planner that surpasses traditional scheduling tools. The paper integrates theories of evolutionary learning algorithms, artificial intelligence, and personalized education, leveraging advanced technologies to dynamically adapt to each user’s unique learning style and preferences, providing a responsive and tailored educational and professional management system. Aligned with SMART criteria, the planner is designed to be specific, tailoring schedules to individual profiles. Progress is measured, evaluating how this impacts academic performance and overall productivity. The planner is crafted to be achievable, creating realistic schedules that accommodate various responsibilities. Time-bound objectives encourage effective time management and reward consistent effort. A SWOT analysis underlines the system’s strengths in personalization, adaptable scheduling, and increased efficiency. Opportunities are wide, from serving a diverse audience, including working professionals, to those managing multifaceted commitments. Weaknesses are acknowledged, and continuous efforts are made to refine algorithms for better adaptability with a user feedback-driven approach. Threats include the need for robust data security and user privacy measures. The Intelligent Study Planner anticipates promising results in enhancing academic performance, productivity, and overall time management skills. Users can experience personalized schedules that adapt to their unique learning styles and other diverse commitments, resulting in improved efficiency and a sense of accomplishment. The evolutionary learning algorithm continuously refines the scheduling process based on user feedback, contributing to a dynamic and responsive educational and professional management system. Gamification elements prove effective in promoting regular study habits and enhancing productivity. The novelty lies in the integration of evolutionary learning algorithms, artificial intelligence, and gamification elements to create an adaptive study planner based on personalized parameters. The results indicate that the system significantly improves user efficiency and motivation, contributing to enhanced academic success, thus promoting a healthy study-life balance.