Sleep apnea is a major global health concern that causes disturbed oxygen supply during sleep and poses a number of health hazards. This study compares several machine learning (ML) and deep learning (DL) algorithms for detecting and predicting sleep apnea. We evaluate the performance of a number of classifiers, including Decision Trees (DT), Logistic Regression (LR), Gradient Boosting (GB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes. Our results show that Gradient Boosting (GB) achieves the maximum accuracy at 95% while operating efficiently with a processing time of 0.01 s. Random Forests (RF) and Logistic Regression (LR) both perform well, with 95% and 91% accuracies, respectively. This analysis highlighted GB’s higher accuracy and speed, indicating that it has the potential to be a trustworthy tool for predicting sleep apnea.