The global transition toward renewable energy is accelerating due to climate change concerns, fossil-fuel volatility, and growing electricity demand. However, renewable integration remains constrained by intermittency, grid instability, and the need for reliable and affordable energy delivery. This research paper proposes and evaluates an AI-assisted hybrid renewable microgrid architecture integrating solar photovoltaic (PV), wind generation, battery energy storage (BESS), and grid support. The core contribution of the work is a predictive Energy Management System (EMS) based on Model Predictive Control (MPC), designed to minimize operational cost, reduce renewable curtailment, and ensure supply reliability under variable demand and generation conditions. The methodology includes renewable forecasting, load modeling, microgrid power-flow simulation, and scenario-based comparison between (i) a baseline system without EMS, (ii) a rule-based EMS, and (iii) the proposed MPC-based EMS. Results show that the proposed EMS reduces Levelized Cost of Energy (LCOE) from 0.142 $/kWh (baseline) to 0.108 $/kWh, decreases renewable curtailment from 9.8% to 3.4%, and lowers CO₂ intensity from 412 gCO₂/kWh to 186 gCO₂/kWh. The study also highlights how optimal scheduling of storage and grid exchange significantly improves both cost and carbon performance. The findings demonstrate that advanced EMS strategies can improve renewable penetration while ensuring reliability, making hybrid renewable microgrids a practical pathway for sustainable electrification in urban and rural settings.