Integrating Artificial Intelligence (AI) and Machine Learning (ML) into renewable energy systems and industrial process control is reshaping the pursuit of sustainability and operational reliability. This paper brings a new perspective by focusing on how AI can support sustainable development in Libya’s energy and industrial sectors. It explores using AI-driven forecasting models to predict better energy production and demand—an essential step toward improving grid reliability in a country with emerging renewable infrastructure. Additionally, it applies Deep Reinforcement Learning (DRL) to industrial process control, showing how intelligent systems can adapt in real-time to enhance efficiency, reduce energy consumption, and minimize waste. The paper contributes in several ways: first, by examining the broader role of AI and ML in improving the performance, resilience, and environmental impact of energy and process systems; second, by providing analysis and practical examples of AI applications and their effectiveness; and third, by offering a specific regional focus on Libya, highlighting the potential for these technologies to be scaled and adapted to local needs. Through case studies and a review of emerging innovations, this work demonstrates how AI-powered solutions can address key challenges in energy production and industrial operations, paving the way for smarter, more sustainable progress in Libya and beyond.