The growing use of machine learning (ML) algorithms in clinical applications is examined in this systematic review, which gives a thorough picture of current developments and suggests possible directions for further investigation. While many ML algorithms are analyzed in the study, well-known ones like Support Vector Machine (SVM), Random Forest (RF), XGBoost, and Logistic Regression are highlighted. The review emphasizes how ML algorithms are being used more frequently in clinical settings and how RF has become a popular ensemble algorithm for efficiently managing big predictor variables in high-dimensional feature spaces. There are issues with computing costs when dealing with larger datasets. Gradient boosting algorithms offer a different strategy that works well on large datasets. The researcher conducted a systematic search for studies pertaining to the application of machine learning algorithms to the diagnosis of human diseases in the medical field using databases such as Scopus, Eric, and Science Direct. Thirty-five articles were fully screened and selected for review. Two popular algorithms that are particularly useful for classifying high-dimensional features and predicting categorical outcomes are Support Vector Machines (SVM) and Logistic Regression. SVM’s interpretability and parameter tuning complexity are acknowledged, but its efficacy in managing heterogeneous medical record datasets is highlighted. The study highlights the need for additional research to address disparities in reporting metrics and uneven adherence to quality assessment checklists, as well as to integrate machine learning into clinical workflows. It is advised that future research standardize reporting procedures and improve repeatability and dependability.