Scheduling remains a complex challenge for event managers, particularly in large-scale contexts such as conferences, cultural festivals, and academic competitions where multiple institutions must coordinate resources under strict operational rules. Traditional scheduling approaches, which rely heavily on manual planning and fixed optimization methods, often lead to conflicts and inefficient resource utilization. Moreover, these systems lack adaptability when circumstances change, resulting in static and error-prone schedules. To address these limitations, researchers propose an intelligent event scheduling framework that integrates machine learning with constraint optimization techniques. By leveraging historical scheduling data, the system generates efficient, conflict-free schedules through data-driven decision-making. The framework is composed of three core components: event similarity analysis using machine learning, constraint-based scheduling optimization, and real-time monitoring systems capable of dynamically adjusting schedules. Evaluation of the framework extends beyond conflict reduction, incorporating metrics such as resource utilization efficiency, scheduling stability, and computational performance. Results demonstrate that combining predictive modeling with intelligent constraint handling significantly improves scheduling accuracy and system robustness under varying operational conditions. Importantly, the framework exhibits strong scalability, maintaining consistent performance across diverse event management scenarios as complexity increases. Beyond operational improvements, the system enhances decision-making by uncovering hidden patterns and dependencies not easily detected through manual analysis. This enables organizers to proactively address conflicts, evaluate multiple scheduling alternatives, and select strategies that balance efficiency, feasibility, and participant convenience. Validation through real-world testing in a multi-venue cultural festival confirmed substantial performance gains, including reduced delays and improved participant coordination.