Densely distributed nodes and high data flow rates close to sinks can cause serious problems for WSNs, especially concerning energy consumption and network complexity. As node and channel traffic management is essential for energy efficiency, not much research has been done on how to solve these problems. This paper presented a novel method that uses a Water Wave Game Theory algorithm to identify and characterize traffic areas that use less energy. Based on different network parameters, the algorithm calculates a fitness function that estimates player stability. Mobile sinks and nearby nodes are notified when the fitness level is low, anticipating energy-efficient traffic patterns and implicitly establishing an alarm threshold. Establish the LAFLC algorithm to tackle complex energy-efficient traffic scenarios. This algorithm optimizes system decisions about mobile data collectors, routing, and node mobility by dynamically learning and adapting to the characteristics of energy-efficient traffic. As a result, it eliminates the need for data rerouting and the replacement of multiple traffic nodes when mobile data collectors are in motion. The proposed approach demonstrates a superior Packet Delivery Ratio (PDR) of 99.95%, throughput of 3500bps, energy consumption of 0.39J, reliability of 98.8% and energy efficiency of 99.9% compared to existing techniques.