Personalized medicine is the customizable approach to medical treatment and healthcare decisions for individual patients based on their unique genetic, environmental, and lifestyle factors. Integrating Artificial Intelligence (AI) into personalized medicine could improve this diagnostic trend. AI predictive models have shown significant promise in diagnosing Pulmonary Hypertension (PH). PH is a complex and often underdiagnosed condition associated with significant morbidity and mortality. Early diagnosis, accurate risk stratification, and personalized treatment are critical for improving patient outcomes in this rare disease. Our review primarily focuses on the currently available predictive AI models for the early detection of Pulmonary Hypertension (PH) using electronic health records. We also emphasize the importance of advanced AI tools integrating additional features, such as genomics. Specifically, we discuss the use of machine learning techniques, including both supervised and unsupervised approaches. Despite the potential of AI predictive tools to transform early detection of PH, challenges remain in effectively integrating them into clinical workflows and interpretation. These challenges arise from issues such as the availability of large, unintegrated datasets, unclear definitions of clustered data, a lack of external validation, and the ineffective use of unstructured data, such as clinicians' notes.