Medical devices and technologies are pivotal to modern healthcare, yet their development, evaluation, and adoption demand methods that differ substantially from pharmaceuticals. This chapter provides a comprehensive, practice-oriented framework spanning needfinding, human-factors engineering, prototyping, preclinical and clinical development, regulatory strategy, manufacturing scale-out, commercialization, and lifecycle safety oversight. We emphasize risk-proportionate evidence generation; socio-technical integration of usability, workflow, and systems interoperability; and postmarket learning using unique device identifiers (UDIs), registries, and real-world evidence. Historical milestones in cardiac pacing, endoscopy, and medical imaging yield durable design heuristics—reliability, ergonomics, and physics–informatics co-design—while current inflections in software as a medical device (SaMD) and AI/ML-enabled tools extend safety cases into data, models, and update governance. Regulatory pathways in the United States and European Union are compared with attention to AI-specific policies (e.g., predetermined change control), and ethical and equity imperatives are operationalized through representative datasets, inclusive human-factors testing, and affordability. Practical guidance is provided for aligning clinical value propositions with reimbursement, integrating manufacturing quality with surveillance, and governing AI model updates in the field. The result is an end-to-end blueprint for translating engineering advances into sustained, equitable patient benefit.