Energy efficiency remains a key issue in wireless sensory networks (WSNs) with limited energy resources of sensory nodes and drawbacks of normal protocols such as LEACH. To overcome these limitations, we provide a hybrid routing protocol that combines optimized data collection, combining static and dynamic agents. Static agents, called local data collector agents (LDCAs), are selected using a system based on ambiguous decisions that takes into account important parameters, particularly residual energy, central nodes, agent speed, and provide a balanced and effective grouping. To expand coverage and minimize energy holes, dynamic agents—Global Data Collector Agents (GDCAs)—follow a dual mobility model comprising spiral and star patterns. Additionally, a Random forest training model is included using the same parameters for rational optimization of decision-making to increase the accuracy and adaptation of LDCA selection. Integration of autolearning not only improves agent selection, but also improves protocol scalability and responsiveness under dynamic network conditions. Modeling results show that the proposed protocol significantly reduces energy consumption and improves network stability and adaptability compared to LEACH. This dual-agent architecture lays the groundwork for dynamic decision- making in future WSN deployments while ensuring quality- of-service-aware transmission and extended network lifespan.