This systematic review presents a critical analysis of advanced machine learning (ML) and deep learning (DL) approaches for predicting the remaining useful life (RUL) of electric vehicle (EV) batteries. Conducted in accordance with PRISMA guidelines and using a novel adaptation of the Downs and Black (D&B) scale, this study evaluates 89 research papers and provides insights into the evolving landscape of RUL estimation. Our analysis reveals an evolving landscape of methodological approaches, with different techniques showing distinct capabilities in capturing complex degradation patterns in EV batteries. While recent years have seen increased adoption of DL methods, the effectiveness of different approaches varies significantly based on application context and data characteristics. However, we also uncover critical challenges, including a lack of standardized evaluation metrics, prevalent overfitting problems, and limited dataset sizes, that hinder the field’s progress. To address these, we propose a comprehensive set of evaluation metrics and emphasize the need for larger and more diverse datasets. The review introduces an innovative clustering approach that provides a nuanced understanding of research trends and methodological gaps. In addition, we discuss the ethical implications of DL in RUL estimation, addressing concerns about privacy and algorithmic bias. By synthesizing current knowledge, identifying key research directions, and suggesting methodological improvements, this review serves as a central guide for researchers and practitioners in the rapidly evolving field of EV battery management. It not only contributes to the advancement of RUL estimation techniques but also sets a new standard for conducting systematic reviews in technology-driven fields, paving the way for more sustainable and efficient EV technologies.