As global energy consumption rises and fossil fuel resources continue to diminish, the use of effective renewable energy technologies becomes increasingly critical. This comprehensive analysis examines maximum power point tracking (MPPT) approaches for solar photovoltaic (PV) installations, evaluating both classical methods, such as perturb and observe and incremental conductance, alongside advanced intelligent approaches, including artificial neural networks (ANNs). The study also explores optimization-based approaches such as particle swarm optimization (PSO) and combined frameworks that integrate ANN with PSO, assessing their effectiveness across different solar irradiance and temperature scenarios. Classical MPPT approaches are known for their ease of implementation; however, they are reported to demonstrate limitations, including slow response times and steady-state oscillations during rapid changes in environmental conditions. In contrast, artificial intelligence and swarm intelligence methodologies show enhanced flexibility, precision, and stable performance across various irradiance conditions. The integration of intelligent algorithms with optimization techniques results in accelerated convergence rates, improved tracking precision, and enhanced stability, consistently maintaining efficiency levels exceeding 99 % while minimizing oscillations. Recent developments in MPPT technology underscore the exceptional adaptability and energy harvesting potential of hybrid methodologies, emphasizing their crucial role in optimizing PV system performance and supporting sustainable power generation.