Electric wheel loaders (EWLs) have emerged as a pivotal innovation in the 2020s, representing a transformative shift toward high-efficiency, low-emission construction machinery. Despite their growing technological and environmental significance, a systematic synthesis of advancements in EWL design, energy optimization, and intelligent control remains absent in the literature. To bridge this gap, this review critically evaluates over 140 studies for comparative analysis. Building on the authors’ ongoing research, this paper categorizes EWL architectures and examines breakthroughs in hydraulic systems, drivetrain configurations, and bucket dynamics optimization. A dedicated focus is placed on energy-saving strategies, including advancements in battery technology, fast-charging infrastructure, intelligent torque distribution, and data-driven modeling of shoveling and operational resistance. The analysis reveals that integrating optimal control strategies with machine learning algorithms—such as model predictive control (MPC)—is a critical pathway to achieving energy-efficient and assisted driving in next-generation EWLs. Furthermore, this review advocates for the adoption of distributed electro-hydraulic drive systems to minimize hydraulic losses and enable efficient energy recovery during actuator control. By synthesizing these insights, this work not only highlights current technological frontiers but also proposes actionable research directions to accelerate the commercialization of intelligent, sustainable EWLs.