The escalating global demand for electricity, coupled with the environmental implications associated with the utilization of fossil fuels, has catalysed the adoption of non-conventional energy technologies, with a specific importance on photovoltaic systems. Standalone PV systems are instrumental in providing electrical power to isolated and rural locales; nevertheless, their efficacy is profoundly influenced by the nonlinear current–voltage characteristics as well as the continual fluctuations in solar irradiance and ambient temperature. These elements render the precise execution to tracking the Maximum Power Point is the formidable challenge under real-time operational scenarios. Traditional MPPT methodologies frequently encounter issues related to sluggish convergence and persistent steady-state oscillations, whereas intelligent control systems necessitate meticulous parameter optimization to guarantee dependable performance. This manuscript introduces a Modified Water Cycle Optimization tuned Artificial Neural Network controller aimed at optimizing MPPT in standalone PV implementations. The ANN is specifically engineered to predict the optimal PV voltage, with its weights and biases being systematically fine-tuned through the MWCO algorithm to enhance convergence precision and circumvent local minima. The suggested methodology is substantiated through MATLAB/Simulink models and is juxtaposed with five pre-existing MPPT strategies under variable environmental conditions. The findings reveal a notable enhancement in tracking efficiency, expedited response times, and minimized power fluctuations, thereby affirming the efficacy of the MWCO-based ANN controller in achieving superior performance in PV systems.