<p>Over the past few decades, credit scoring has become an important tool in the financial sector. It enables banks and financial institutions to assess the creditworthiness of individuals and reduce the risk of default. As a result of significant advances in artificial intelligence techniques. Machine learning (ML) has made it possible to improve credit scoring by distinguishing between people with good creditworthiness and those with poorer creditworthiness. In this article, we propose a systematic literature review of ML-based financial credit scoring methods published between 2018 and 2024. A total of 330 research papers were extracted from four different online databases and digital libraries. After the study selection procedure, 63 research papers were selected for this systematic review. This paper aims to identify the major ML methods used in credit scoring, assess their strengths and limitations, and highlight notable trends and advancements. In addition, the review addresses the critical challenges faced in the adoption of ML models for credit scoring. This study not only contributes to the understanding of effective ML techniques used for credit scoring but also guides future research by highlighting the promising avenues in ML-based credit scoring efforts.</p>