The transition to renewable energy, mainly via the use of PV (photovoltaic) systems, is essential for addressing global concerns related to climate change, energy security, and sustainability. Conventional Maximum Power Point Tracking (MPPT) techniques, particularly Perturb and Observe (P&O) and Incremental Conductance methods, rely on fixed-step gradient-based control, which leads to steady-state oscillations around the maximum power point, slow convergence during rapid irradiance and temperature variations, and inaccurate tracking under partial shading conditions. These technical limitations often cause the controller to deviate from the global maximum power point, resulting in reduced dynamic efficiency, increased power losses, and degraded power quality in practical PV applications. To overcome these limitations, this research proposes a hybrid optimization model that incorporates ACO (Ant Colony Optimization) and an ANN (Artificial Neural Network) to enrich the effectiveness of MPPT in PV systems. The proposed model is designed to dynamically adapt to variations in solar irradiance and temperature, effectively addressing the inadequacies present in the conventional techniques and also improving the MPPT efficiency in PV systems. By leveraging the unique strengths of both ACO and ANN, the model significantly improves energy extraction and also ensures robustness against environmental fluctuations. Simulation results demonstrate that the proposed ACO–ANN MPPT framework achieves a total harmonic distortion (THD) of 1.39%, representing a reduction of approximately 34–70% compared to conventional and recent AI-based MPPT techniques, while simultaneously delivering higher voltage stability, faster convergence, and increased maximum power extraction. This contribution is vital in paving the way for future advancements in renewable energy systems and provides a more reliable approach to solar power optimization, which can greatly aid in achieving sustainable energy goals.