The Use of Machine Learning Algorithms in Disease Prediction In light of the rapid advancement of artificial intelligence technologies, machine learning algorithms have emerged as powerful tools in supporting the healthcare sector and improving the quality of medical services. Early disease prediction is considered one of the most significant applications that has brought a qualitative shift in diagnostic and treatment methods, due to its major role in reducing mortality rates and increasing recovery rates. Machine learning relies on analyzing large volumes of medical data, such as electronic health records, laboratory test results, and medical imaging, to detect hidden patterns and relationships that may not be evident through traditional methods. By training computational models on historical data, systems can be developed to predict the likelihood of a patient developing a specific disease before symptoms clearly appear. Several algorithms are widely used in this field, including: • Logistic Regression for predicting chronic diseases. • Decision Trees for classifying medical cases. • Random Forest to enhance prediction accuracy and reduce error rates. • Artificial Neural Networks for analyzing complex medical data and imaging. • Deep Learning algorithms for diagnosing cancer and cardiovascular diseases. Studies have demonstrated that integrating machine learning algorithms with electronic healthcare systems contributes to: • Improving diagnostic accuracy. • Reducing the time required for medical decision-making. • Supporting physicians with precise predictive indicators. • Lowering treatment costs through early detection. Despite the significant advantages of these technologies, several challenges remain, including ensuring the privacy and security of medical data, maintaining high-quality datasets, and providing advanced technical infrastructure along with specialized data analysis expertise. The application of machine learning algorithms in disease prediction represents a strategic step toward building intelligent healthcare systems capable of delivering data-driven, proactive care. This approach also reinforces the role of academic institutions in advancing scientific research and developing technological solutions that serve society. These topics are among the most prominent contemporary research interests in computer science and artificial intelligence, given their direct impact on improving quality of life and supporting sustainable development.