This review paper tries to explore the efficacy of various machine learning algorithms and deep learning algorithms in predicting factors such as employee attrition, with a particular focus on studies that make use of the IBM HR analytics employee attrition dataset. Among other algorithms that were used including Random Forest, logistic regression, Naïve Bayes, SVM, decision trees, and gradient boosting. Random forest and logistic regression have consistently shown good results in most of the studies performed, with many studies showing high accuracy rates among them. In addition, the performance of deep learning models was commendable, with considerable accuracy levels attained. Techniques such as voting ensembles and synthetic minority over-sampling technique (SMOTE) were identified as capable of improving model performance by creating more balanced datasets. Thus, the highest accuracy of prediction was achieved with the help of the decision tree model. Consequently, based on this research, it is viable to state that the prediction of employee attrition could be controlled by applying distinct types of machine and deep learning approaches. Logistic regression and random forest appear to have been rather useful, while the others showed some utility to a certain degree, such as ensemble methods and deep learning. These algorithms can predict employee turnover effectively, as substantiated in this extensive literature review, which is useful as a guide for further studies.