This chapter primarily discusses how to link different sources of data to better infer youth’s or adolescent’s behavioral and psychological patterns and support them with varying levels of service. This ranges from different stages of treatment, processing insurance claims to assisting individuals and their parents with assistive services through the use of knowledge graphs (KGs). This chapter lays a solid foundation of using knowledge graphs (like Neo4J) which play a prominent role in interlinking descriptions of concepts, entities, relationships, and events. This chapter helps us to dig a step deeper to decipher the relationship between different nutrients and medications, understand how natural language processing (NLP) plays a key role in extracting named entity recognition (NER), and derive decisions for further medications and treatments. This chapter further emphasizes on identifying similar behavioral patterns in youth and offering them existing treatment plans and recommendations that work well in the community. With regard to designing a faster and more efficient youth care 360-degree service, this chapter familiarizes readers with knowledge graphs, ontologies, and their importance in the end-to-end pipeline right from prediction, to taking action to complete diagnosis, cure, and rehabilitation process. By equipping youth, parents, and doctors with a framework, this chapter provides a solid foundation where readers will get to understand how data and AI using knowledge graph (KG) can assist in early detection and intervention.