<p>The operational efficiency of photovoltaic (PV) systems directly impacts their economic viability and environmental benefits. This study presents a novel hybrid intelligent controller for Maximum Power Point Tracking (MPPT) that improves energy harvesting and sustainability by integrating Support Vector Regression (SVR) predictive capabilities with Q-Learning adaptive control. The SVR component, trained on comprehensive simulation data covering irradiance ranges of 200–1000 W/m² and temperature ranges of 15–45°C, accurately predicts maximum power points based on environmental inputs. The Q-Learning component refines these predictions in real time, demonstrating superior adaptability to dynamic operating conditions and system aging. Extensive validation confirms exceptional performance: SVR models achieve high predictive accuracy (R² = 0.9823 for voltage; R² = 0.9889 for power), while the hybrid controller attains 99.81% tracking efficiency, exhibiting 59% better degradation resistance compared to conventional approaches. For a representative 10 kW PV installation, the method yields substantial benefits: 738 kWh/year of additional energy generation (a 4.4% improvement), 516 kg of annual CO₂ reduction (12.9 metric tons over 25 years), a rapid 3.6-year payback period, and a net lifetime profit of $2,963. The conservative base-case Levelized Cost of Energy (LCOE) analysis demonstrates minimal change (-0.42%). In contrast, sensitivity analysis confirms that efficiency gains exceeding 5% result in substantially reduced LCOE. These findings establish the hybrid SVR-Q-Learning approach as a commercially viable and scalable solution that addresses the dual imperatives of maximizing renewable energy yield while ensuring robust long-term economic returns, positioning the approach as a practical advancement for enhancing global PV infrastructure efficiency and reducing environmental impact.</p>