Mutation testing evaluates test suite effectiveness by introducing small code modifications (“mutants”) and checking if tests detect them. A major challenge is equivalent mutants, which are code changes that do not alter program behavior, making detection computationally expensive and undecidable. This review examines Machine Learning approaches, including Abstract Syntax Tree Neural Networks and Tree-Based Convolutional Neural Networks, to address this issue. Key challenges include limited datasets, inconsistent metrics, and reproducibility concerns. The study highlights the need for standardized frameworks and future research directions in mutation testing.