Personalized disease prediction has become a paramount research area in healthcare, aiming to enhance early diagnosis and preventive interventions. Machine learning algorithms have emerged as powerful tools in this domain, leveraging vast datasets to develop accurate and tailored prediction models. This study presents a thorough examination of machine learning techniques used in personalized illness prediction that do not depend on individual data points. The study explores various techniques, including ensemble methods, deep learning networks, and feature selection algorithms, highlighting their strengths and limitations in the context of personalized healthcare. Key challenges, such as data privacy, model interpretability, and algorithmic biases, are discussed. Furthermore, this review emphasizes the importance of adopting standardized evaluation metrics and robust validation strategies to ensure the reliability and generalizability of personalized disease prediction models. The paper concludes with future directions, emphasizing the potential of integrating multimodal data sources and advancing explainable AI techniques to enhance the practical applicability of machine learning algorithms in personalized disease prediction.