The collection of interconnected computers that constitutes more than one united computing resource is known as the Cloud. In recent years, the advancement of cloud computing has facilitated the rapid arrangement of interconnected data centers that are geographically dispersed, offering high-quality and dependable services. Scalable traffic management has recently been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, reducing latency during multidimensional resource allocation still remains a challenge. Hence, there is a need for efficient resource scheduling to ensure load optimization in the cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The goal of the proposed system is to select the required load balancing algorithm to enhance resource utilization. Simulations were conducted to evaluate effectiveness using the Cloudsim simulator in cloud data centers, and the results show that the proposed method achieves better performance in terms of the average success rate, resource scheduling efficiency, and response time. The dynamic nature of cloud environments requires constant adaptation in resource allocation strategies. This necessitates the development of algorithms capable of handling diverse workloads efficiently. Additionally, the increasing complexity of applications and services hosted on the cloud demands a comprehensive approach that considers not only load balancing but also the intricacies of resource utilization. Furthermore, the proposed algorithm focuses on predictive analytics to anticipate fluctuations in demand and adjust resource allocation preemptively. By incorporating machine learning techniques, the system can adapt to changing patterns, ensuring optimal performance even in unpredictable scenarios. This holistic approach addresses the evolving challenges in cloud computing, providing a robust foundation for reliable and efficient service provisioning.