Chronic diseases — including cardiovascular disorders, diabetes mellitus, cancer, and chronic kidney disease — represent a leading cause of global morbidity and mortality. Early detection remains the single most effective strategy to reduce disease burden and improve patient outcomes. Machine learning (ML) has emerged as a transformative paradigm in clinical medicine, enabling the construction of predictive models that surpass traditional risk-scoring tools in both accuracy and scalability. This article reviews the principal ML algorithms applied to chronic disease detection, compares their performance using standard metrics (accuracy, precision, recall, and area under the receiver operating characteristic curve [AUC]), and examines deep learning architectures including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. Clinical risk assessment frameworks and the translation of predictive models into preventive healthcare are also discussed. Evidence suggests that ensemble methods and deep learning consistently achieve AUC values exceeding 0.90 across multiple disease domains, signalling a clinically meaningful improvement over conventional approaches.