The global shift to electric vehicles (EVs) is a critical step towards developing sustainable transportation and lowering greenhouse gas emissions. Despite this pace, mainstream EV adoption is still hampered by range anxiety, the fear that the vehicle may fail to reach its destination due to insufficient battery charge. Accurate range assessment is thus critical for increasing driver trust, optimising energy management, and enabling wider EV adoption. Traditional prediction methods, which are generally based on mathematical or physics-driven models, employ static assumptions and struggle to account for the influence of dynamic factors such as driver behaviour, road geometry, traffic congestion, and weather unpredictability. This paper examines the use of machine learning (ML) techniques for EV range estimate and compares them to traditional methods in terms of accuracy, adaptability, and real-world applicability. A wide range of machine learning models have been evaluated, including supervised learning methods, deep learning architectures like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, and developing hybrid and ensemble approaches. These models excel at capturing non-linear correlations by continuously learning from a variety of datasets that include sensor telemetry, history trip data, and environmental inputs. Performance evaluation criteria such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are used to benchmark prediction quality. In addition to emphasising advancements in predicting accuracy, the paper delves into major implementation issues such as data availability, computational efficiency, model interpretability, and user privacy. Promising options such as federated learning, transfer learning, explainable AI, and edge deployment are also considered. The insights offered are intended to help researchers, engineers, and policymakers construct robust, scalable, and ethically acceptable EV range estimation systems, hence promoting the evolution of intelligent and sustainable electric mobility.