Synthetic healthcare data offers a transformative solution to the privacy and accessibility constraints of real patient data. This chapter comprehensively examines its generation, from traditional statistical methods to advanced AI like GANs, VAEs, and Transformers. It explores critical applications in clinical trial simulation, medical imaging, and AI training. The analysis extends beyond technology to address pivotal barriers: ethical concerns of bias, evolving GDPR/HIPAA regulations, validation challenges, and interoperability with systems like HL7 FHIR. It concludes that responsible adoption hinges on merging technical rigor with ethical governance and cross-disciplinary collaboration to build trustworthy, innovative digital health ecosystems.