<p>This review synthesizes research on machine learning algorithms effectiveness in detecting discriminatory patterns in mortgage lending, focusing on decision trees, neural networks, and support vector machines, to address challenges in bias identification and mitigation. The review aimed to evaluate algorithmic effectiveness, benchmark bias detection and mitigation approaches, analyze fairness metrics, compare model interpretability, and examine challenges in addressing intersectional biases. A systematic analysis of empirical studies primarily from the United States employing diverse mortgage datasets and fairness evaluation frameworks was conducted. Findings indicate that decision trees offer high interpretability and effective bias detection, while neural networks and support vector machines achieve superior predictive accuracy but suffer from low transparency. Fairness metrics reveal persistent biases despite high accuracy, and bias mitigation techniques such as reweighting and fairness constraints reduce discrimination but often entail trade-offs with model utility. Intersectional fairness remains underexplored, with limited integration of multi-attribute bias assessments. Overall, balancing accuracy, fairness, and interpretability remains a critical challenge, with explainable AI methods enhancing transparency in complex models. These findings underscore the need for standardized fairness evaluation, improved intersectional bias detection, and transparent algorithmic design to advance equitable mortgage lending practices.</p>