The global shift toward renewable energy has necessitated the development of efficient power generation methods to address the intermittency and variability of sources like solar photovoltaic (PV) systems. The performance of PV systems is significantly influenced by Maximum Power Point Tracking (MPPT) algorithms, which ensure optimal power extraction. This study compares two widely adopted MPPT algorithms: Perturb and Observe (P&O) and Incremental Conductance (IC). Both methods are tested under varying environmental conditions, such as irradiance and temperature, using MATLAB/Simulink simulations to assess their efficiency and accuracy in tracking the Maximum Power Point (MPP). While P&O is simpler and commonly used in commercial systems, it oscillates around the MPP, leading to potential power loss. In contrast, the IC algorithm provides greater precision but requires higher computational resources. Simulation results showed that both algorithms effectively tracked the MPP under controlled conditions, though IC demonstrated a more stable performance during rapid changes in irradiance and temperature. This research offers practical insights into the suitability of these MPPT algorithms for specific PV applications, contributing to improved efficiency in renewable energy systems.