<p>Wireless Sensor Networks (WSNs) play a crucial role in various applications, ranging from environmental monitoring to industrial automation, by enabling remote sensing and data collection from diversified environments. However, the resource-constrained nature of sensor nodes coupled with dynamic network conditions poses significant challenges in optimizing load balancing, energy utilization, and data transmission efficiency. Conventional approaches suffer from issues such as uneven energy consumption, congestion, and suboptimal routing, which leads to degraded network performance. This research introduces a comprehensive approach, termed Proactive Load Balancing and Distributed Data Aggregation Scheduling (PLB-DDAS), aimed at enhancing the efficiency and performance of WSNs. The proposed PLB-DDAS adopts a multi-route selection strategy and congestion governance method to achieve load balancing in WSNs. The algorithm dynamically allocates time slots to sensor nodes based on their residual energy, distance from the sink, and number of neighbors, thereby optimizing energy utilization and reducing transmission delays. Additionally, PLB-DDAS employs a distributed data aggregation scheduling mechanism to aggregate data efficiently while minimizing energy consumption and transmission latency. From the simulation results it has been identified that the performance of PLB-DDAS is better than the existing algorithms in terms of goodput, end-to-end delay, packet delivery ratio, energy consumption, network lifetime, data loss, and conflict rate.</p>