In the era of precision medicine, the integration of predictive analytics and machine learning algorithms holds immense promise for revolutionizing healthcare delivery. This abstract presents an overview of the application of predictive analytics in precision medicine, focusing on personalized diagnosis, treatment planning, and patient outcome prediction. Harnessing the power of machine learning, predictive analytics enables healthcare practitioners to analyze vast amounts of patient data, including genomic information, electronic health records, medical imaging, and lifestyle factors, to tailor interventions to individual patients. By leveraging advanced algorithms, such as deep learning, support vector machines, and random forests, predictive models can extract meaningful insights from heterogeneous data sources, facilitating the identification of biomarkers, disease subtypes, and optimal treatment strategies. Personalized diagnosis represents a cornerstone of precision medicine, aiming to stratify patients based on their unique genetic makeup, clinical profiles, and environmental exposures. Machine learning algorithms enable the development of diagnostic models capable of accurately predicting disease onset, progression, and recurrence, empowering clinicians to intervene proactively and customize care plans to individual patient needs. Furthermore, predictive analytics plays a pivotal role in treatment planning by guiding therapeutic decision-making and optimizing drug selection, dosage, and administration schedules. By integrating clinical data with predictive models, healthcare providers can anticipate treatment responses, identify potential adverse reactions, and tailor interventions to maximize efficacy while minimizing side effects.