This study proposes an intelligent Maximum Power Point Tracking (MPPT) technique for a grid-connected hybrid renewable energy system (HRES) that integrates photovoltaic (PV) and wind energy conversion system (WECS). The novel approach is based on the crow search algorithm (CSA), a bio-inspired optimization method aimed at improving energy extraction efficiency at fluctuating environmental conditions. In such hybrid systems, conventional MPPT algorithms often encounter difficulties due to nonlinear system behavior and the intermittent nature of resources, particularly under partial shading or turbulent wind patterns. To overcome these issues, CSA is implemented to dynamically track the global maximum power point (GMPP) across both the variable-speed wind turbine and the PV array under shade. The proposed controller is assessed using MATLAB/Simulink simulations, with its performance benchmarked against the widely used Particle Swarm Optimization (PSO) technique. The simulation results suggest that the CSA-based method offers faster convergence, higher tracking precision, and improved overall system stability. These outcomes point to CSA as a promising option for real-time MPPT control in hybrid renewable energy systems, though practical validation remains an important future step