Research papers in machine learning for disease prediction focus on developing intelligent models that analyze medical data to forecast the onset, progression, or risk of various diseases. These works apply supervised and unsupervised learning algorithms such as support vector machines (SVM), random forests, decision trees, logistic regression, k-nearest neighbors (KNN), ensemble methods, and deep learning models (CNN, RNN, LSTM, and transformer architectures) to structured datasets like electronic health records (EHRs), sensor data from IoMT devices, genomic sequences, and unstructured data such as medical images and clinical notes. Key areas of application include early detection of chronic illnesses like diabetes, cardiovascular disease, and cancer, as well as neurological disorders, infectious diseases, and rare genetic conditions. Research also highlights challenges in data quality, missing values, imbalanced datasets, and the need for explainable AI to ensure trust in clinical decisions. Recent studies explore federated learning for privacy-preserving disease prediction, multimodal learning that integrates imaging and textual records, and predictive models that support personalized medicine. By enabling accurate and timely disease forecasting, machine learning significantly enhances clinical decision-making, reduces healthcare costs, and improves patient outcomes.