Optimizing the performance of Mobile Wireless Sensor Networks (MWSNs) requires efficient cluster formation, Cluster Head (CH) selection, feature selection, and data forwarding strategies to improve communication efficiency and energy usage. This paper proposes an Optimized Cluster-Based Communication (OCBC) framework for MWSNs, integrating Fuzzy Neural Networks (FNN) and the Crow Search Algorithm (CSA). The CSA algorithm is used to guide cluster formation and CH selection by modeling the intelligent behavior of crows, resulting in energy-efficient partitioning of the network. An FNN-based mechanism is then applied to identify optimal forwarding nodes by combining fuzzy logic with neural learning. The decision process considers key parameters such as node energy levels, communication range, reliability, and transmission efficiency. This adaptive method addresses the unpredictability of MWSNs, enabling robust and energy-aware data transmission. Experimental results show that the proposed OCBC approach significantly reduces network energy consumption, improves data forwarding accuracy, and enhances communication reliability, making it well-suited for real-time applications.