The global demand for sustainable and high-performance electrical systems has intensified the focus on designing energy-efficient electric machines, particularly in industrial automation and renewable energy conversion. These machines serve as the backbone of modern industries and play a critical role in decarbonizing energy systems by replacing fossil-fuel-based operations. From a broad perspective, electric machines such as motors and generators are integral to over 70% of industrial energy consumption and significantly impact global energy efficiency targets. The transition to smarter, greener production environments necessitates machines that are not only highly efficient but also optimized for various load profiles, environmental constraints, and operational reliability. This study delves into the advanced design principles and multi-objective optimization strategies for enhancing the efficiency, performance, and reliability of electric machines used in industrial and renewable settings. It highlights recent innovations in magnetic materials, thermal management, winding configurations, and rotor-stator topologies that contribute to loss minimization and power density improvement. Finite Element Method (FEM)-based modeling, AI-driven design optimization, and real-time control integration are discussed as key enablers for tailoring machines to specific application demands. Furthermore, the study examines the role of permanent magnet synchronous machines (PMSMs), switched reluctance motors (SRMs), and brushless DC machines (BLDCs) in driving industrial automation and powering renewable sources such as wind turbines and solar tracking systems. The paper also addresses the economic, environmental, and lifecycle assessment considerations in machine design, thereby aligning engineering innovation with global sustainability goals. Ultimately, this research advocates a holistic and application-specific approach to electric machine development, enabling smarter energy systems and more resilient automation infrastructures.