The University Class Scheduling Problem stands as a pivotal and intricate challenge within academia. A proficient university timetable holds the key to seamless academic operations, necessitating the delicate balance of multiple constraints like teacher availability and subject-hour allocations. This NP-Hard problem's complexity arises from its diverse limitations. Manual timetable creation presents a formidable task for educators and evaluators alike, underscoring the need for a system that fosters a balanced and equitable learning environment. This study introduces a novel university timetable scheduling algorithm adept at accommodating dynamic constraints, thus addressing the demand for adaptable and responsive scheduling solutions. By harnessing the potential of heuristic and machine learning algorithms, our Timetable Scheduling System aims to efficiently generate schedules while adhering to specified constraints that should not be compromised, ensuring the production of high-quality timetables. Ultimately, this endeavor strives to streamline the timetable creation process, liberating educators to focus on delivering superior education and optimizing student outcomes.