The adoption of EHRs (Electronic Health Records) within the healthcare system has greatly improved data accessibility as well as clinical decision-making support. Despite these advancements, challenges in data integration, interoperability, and usability persist, limiting the full potential of EHRs. Personal Health Knowledge Graphs (PHKGs) have emerged as a transformative and revolutionary solution, offering a structured, semantic framework to unify heterogeneous health data sources, including EHRs, wearable devices, genomic data, and lifestyle information. This paper provides a comprehensive exploration of PHKG construction, focusing on standardized ontologies, statistical analysis, and natural language processing (NLP) for knowledge extraction. We emphasize their applications in clinical decision support, ongoing health monitoring, personalized recommendations, and disease prediction. While helpful as they are, PHKGs face challenges related to scalability, privacy, and system integration. Future advancements in artificial intelligence (AI), the Internet of Things (IoT), and predictive analytics will further enhance PHKG capabilities, propelling the next wave of personalized and precision healthcare.