ABSTRACT The rapid integration of renewable energy sources into modern power distribution networks introduces significant challenges related to load variability, system reliability, and fault management. This paper presents an AI-Driven Adaptive Load Balancing and Fault Prediction Framework for Smart Renewable Power Distribution Systems designed to enhance operational efficiency and grid stability. The proposed methodology combines Internet of Things (IoT)-enabled sensors, real-time data acquisition, machine learning algorithms, and deep learning-based predictive analytics to continuously monitor power generation, consumption patterns, voltage fluctuations, and equipment health. An adaptive load balancing module dynamically redistributes electrical loads based on demand forecasts and renewable energy availability, while the fault prediction engine identifies potential failures before their occurrence. Historical operational data and real-time measurements are processed through intelligent data preprocessing, feature extraction, and predictive modeling stages to support autonomous decision-making. Experimental evaluation demonstrates that the framework significantly improves load distribution efficiency, minimizes power losses, and enhances fault detection accuracy compared with conventional grid management approaches. Results indicate improved renewable energy utilization, reduced outage duration, faster response to grid disturbances, and increased overall system reliability. The integration of artificial intelligence with smart power infrastructure provides a scalable and sustainable solution for next-generation renewable energy distribution networks, supporting resilient, efficient, and intelligent energy management in dynamic operating environments. Keywords: Artificial Intelligence, Smart Grid, Renewable Energy Distribution, Adaptive Load Balancing, Fault Prediction, Predictive Analytics..