<p>This review critically examines recent advancements in the application of machine learning (ML) algorithms to mechanical and aerospace engineering, providing new insights into their transformative potential across key domains. Beyond summarizing existing approaches, the paper identifies significant improvements in ML methods that address the complex, non-linear nature of Mechanical Engineering problems. Further, highlight breakthroughs in data-driven design, optimization, and automation across areas such as advanced material design, predictive manufacturing, computer-aided engineering (CAE), computational fluid dynamics (CFD), robotics, unmanned aerial vehicles (UAVs), energy systems, and intelligent transportation. This review emphasizes emerging ML techniques, such as deep learning, reinforcement learning, and hybrid models, which have reshaped traditional ME paradigms by enabling higher precision, scalability, and adaptability. A comparative analysis of the strengths and limitations of these methods are presented, offering new conceptual insights into their integration with ME systems. The paper outlines the future trajectory of ML in mechanical and aerospace engineering, forecasting its potential to redefine performance benchmarks and accelerate innovation.</p>