This study presents a comprehensive performance evaluation of an installed rooftop hybrid photovoltaic (PV)-battery system, controlled using multiple Maximum Power Point Tracking (MPPT) algorithms. The system comprises a rooftop PV array integrated with a DC-DC boost converter and a bidirectional converter interfaced with a battery storage unit, ensuring optimal energy flow and power balance under real operating conditions. Five MPPT strategies, Perturb and Observe (P&O), Incremental Conductance (INC), a proposed hybrid P&O-INC, a Neural Network (NN)-based controller, and Sliding Mode Control (SMC), are implemented and comparatively assessed using MATLAB/Simulink. The boost converter regulates the PV voltage to ensure accurate MPP tracking, while the bidirectional converter manages battery charging and discharging cycles. Performance is evaluated under both controlled irradiance profiles and actual atmospheric variations to capture the system's dynamic behavior, energy harvesting efficiency, and operational stability. The results demonstrate that the proposed hybrid P&O-INC-based MPPT controllers outperform conventional methods by offering faster convergence, reduced steady-state oscillations, and improved charging stability. Overall, the findings confirm the effectiveness of hybrid and intelligent MPPT techniques in enhancing energy conversion efficiency and reliability of rooftop PV-battery systems in arid climates.