The development of durable, high-performance materials is vital for advancing the automotive, battery, and nuclear industries, which face extreme operating conditions and complex degradation mechanisms. Traditional methods are often slow and limited, whereas artificial intelligence (AI) and machine learning (ML) offer transformative capabilities for accelerating material development, predicting degradation, and enhancing materials performance. Alloy development for the automotive sector traditionally relied on slow, human-guided compositional design and labor-intensive data analysis, limiting computational validation and material optimization. Recent advances emphasize high-throughput experimental methods and AI/ML-driven frameworks to accelerate alloy design and recycling for a circular economy. ML significantly enhances corrosion prediction accuracy by analyzing large datasets, identifying degradation trends, and refining material performance models. Integrating experimental data with ML-trained models enables rapid estimation of corrosion parameters from alloy composition and microstructure, improving reliability, reducing cost, and expediting material selection processes. AI has revolutionized next-generation energy storage technologies, such as lithium-ion batteries. Its applications span the discovery of advanced electrode materials, the development of novel electrolyte formulations, the optimization of operational and process parameters, and the prediction of key performance metrics, including state-of-health (SOH), state-of-charge (SOC), and battery degradation, at both the cell and pack levels. AL has revolutionized nuclear materials research by enabling predictive insights at both component/system and microstructural levels of existing and advanced reactors. Decades of operational data have shown that AI/ML enhances situational awareness, enabling proactive management of degradation in critical structures, systems, and components. Recent AI advances support anomaly detection, diagnostics and prognostics, autonomous control, and high-throughput material development and qualification. Deep learning models have significantly improved the prediction of radiation-induced damage, while ML-driven simulations accelerate the design of next-generation nuclear reactors using an advanced coolant system.