Load balancing is an important issue in any distributed system or in cloud computing, where optimal performance and stability of the system are chiefly concerned with effective allocation of resources and distribution of workload. This is notwithstanding, due to the failure of certain classical load balancing algorithms, namely, Round Robin and Least Connections, to appreciate parameters such as dynamically changing workloads, heterogeneous capacities of servers, and different sizes of requests, a situation of suboptimal resource utilization and high response times rise. Hence, we propose an advanced version of Weighted Load Balancing with Dynamic Request Distribution (WLDRD) algorithm. WLDRD maintains a dynamic coordination among the weights of the servers, the sizes of incoming requests, and the live load situations to achieve real-time fine-tuning of request distributions depending on the loads and capacities of selected servers. The algorithm's strong point lies in balancing resource usage while keeping minimum server response time. Results from the experiments established that WLDRD gives better results compared to the traditional load balancing approaches with load balancing efficiency increased by 20% and response time decreased by 15% and resource utilization increased by 20%. In addition, the algorithm has the potential to achieve 25% energy savings through effective resource allocation that complies with the principles of green computing. Such an improvement would, of course, be very pronounced when variable workloads and heterogeneous server capabilities are given as input into the system. These results show the potential of this algorithm in improving the performance and scalability of distributed systems and cloud computing environments.