Artificial Intelligence (AI) has emerged as a transformative technology in the field of healthcare, particularly in the areas of predictive analytics and early disease detection. By integrating machine learning algorithms, natural language processing, and deep learning models, AI can analyze vast and complex medical datasets to identify patterns and correlations that are often invisible to the human eye. Predictive healthcare utilizes these capabilities to anticipate disease onset, progression, and patient outcomes, thereby facilitating proactive interventions. The application of AIdriven systems in radiology, genomics, pathology, and clinical diagnostics has led to faster diagnosis, improved accuracy, and personalized treatment plans. This paper explores the growing influence of AI in predictive healthcare, focusing on its ability to detect diseases at early stages, optimize medical decision-making, and support preventive medicine. It also highlights the challenges of data privacy, model interpretability, ethical issues, and integration with existing healthcare systems. The study aims to provide a comprehensive overview of how AI technologies are reshaping modern healthcare practices, leading to cost efficiency, better patient outcomes, and a shift toward data-driven preventive medicine. The integration of artificial intelligence into modern healthcare represents one of the most significant technological evolutions in the twenty-first century. Predictive healthcare, powered by artificial intelligence, enables medical professionals to foresee health risks, identify disease patterns, and recommend timely interventions long before the onset of visible symptoms. Using machine learning, deep learning, and natural language processing, AI systems can process large and complex datasets including medical images, electronic health records, genetic information, and patient histories to generate accurate predictive insights. This capacity allows for early disease detection and personalized medical treatment, leading to improved health outcomes, reduced costs, and efficient allocation of healthcare resources.