This study addresses the challenges of intermittent power supply caused by factors such as renewable resource intermittency, grid infrastructure incompatibility, lack of energy storage systems, frequency and voltage instability, faulty inverter systems, cybersecurity threats, regulatory barriers, operational coordination challenges, and environmental factors. To overcome these issues, the research proposes optimizing renewable energy integration into the grid using advanced machine learning techniques. The methodology involved identifying and characterizing causes of power failures, designing conventional and advanced SIMULINK models, developing machine learning rule bases, and implementing algorithms to optimize grid performance. Validation was performed by comparing results with and without advanced machine learning techniques. Key findings demonstrated significant improvements. Renewable resource intermittency, initially at 30%, was reduced to 26.01%. Grid infrastructure incompatibility decreased from 20% to 17.34%, and frequency and voltage instability dropped from 10% to 8.67%. These results reflect a 1.33% overall optimization in renewable energy integration into the grid. The study highlights the potential of machine learning techniques in enhancing grid reliability and performance. Future work should focus on scaling these solutions for broader applications, incorporating hybrid models, and addressing emerging threats to ensure sustainable and resilient energy systems.