This paper presents an optimized model that combines the Intelligent Water Drop (IWD) Optimization algorithm with a neural network (NN) for maximum power point tracking (MPPT) in PV applications. The novel approach and high performance of the present method are fully demonstrated as well as compared to conventional methods, which include Fuzzy Logic Control, Perturb & Observe (P&O), Particle swarm optimization (PS), Genetic algorithm(GA) and Incremental conductance (INC) control The enhanced model enhances adaptability and convergence because of the IWD algorithm's optimization characteristic, as well as takes advantage of NN's predictive characteristics for tracking with improved speed. The result suggests that this could serve as the breakthrough for future-generation PV system like solar energy applications.