Boosting energy efficiency and extending bandwidth sustainability are major issues in Wireless Sensor Networks (WSN), but these issues have not been addressed in traditional communications research. Random static selection of Cluster heads (CH) in established clustering protocols often leads to inconsistent energy consumption and node failure early on. This paper proposes an intelligent Deep Fuzzy Optimization (DFO) framework for adaptive CH selection. The system integrates Fuzzy Logic to assign a multi-parameter evaluation and Deep Neural Networks (DNNs) to identify CH. The fuzzy module computes and assigns weighted scores based on residual energy, node density, distance to base station, and communication costs of nodes to act as CH. The DNN completes the selection by predicting each node's suitability to act as a CH. The final optimization step evaluates the selected nodes to maximize an aggregate hybrid fitness function. The DFO method provides higher packet delivery ratios, energy efficiency, and total lifetime benefits than traditional LEACH and Fuzzy-LEACH methods.