Abstract Efficient load scheduling algorithms are necessary in cloud computing systems to maintain high performance and manage resources properly. In order to improve efficiency in cloud computing settings, this study investigates sophisticated load scheduling strategies. We examine a range of algorithms, such as AI-based, dynamic, and adaptive scheduling techniques, and assess each one's performance under various conditions. The report also emphasises the difficulties and potential directions for load scheduling in cloud ecosystems. Efficient load scheduling strategies are necessary in cloud computing settings to provide optimal resource management and optimal performance. The dynamic nature of workloads and the need for scalability in these situations render typical scheduling techniques inadequate. This research explores sophisticated load scheduling strategies created especially to maximise resource distribution and raise overall system performance. We perform a thorough analysis of a number of algorithms, such as AI-based scheduling, which uses machine learning and other artificial intelligence techniques to predict and manage workloads proactively; adaptive scheduling, which adapts to changing conditions and workload patterns; and dynamic scheduling, which modifies resources in real-time based on current load. The efficacy of each approach is assessed in a variety of situations, including multi-tenant cloud platforms, diverse resource settings, and variable workload intensities. Our research also reveals important implementation-related obstacles, including computing overhead, integration complexity, and the need for ongoing learning and adaptation. In conclusion, we address load scheduling patterns in the future within cloud ecosystems, highlighting the possibility that more advances in AI and machine learning could spur resource management strategy innovation.