As manufacturing evolves toward smart, interconnected production and autonomous vehicles become more prevalent, the demand for immediate and reliable data handling has never been greater. This thesis explores the dual-sided dynamics of edge computing in these contexts— highlighting its potential to power real-time processing, system resilience, and privacy by conducting computations close to where data is generated. In manufacturing, it enables real-time monitoring and fault detection, reducing downtime and improving throughput. In autonomous vehicles, it supports split-second processing for safe navigation and environmental awareness. However, the adoption of edge computing also presents significant challenges: ensuring consistent interoperability across diverse hardware, managing limited computing and energy resources, maintaining low-latency communication, and safeguarding data integrity in distributed environments. Drawing upon both case studies and emerging deployment models, the work offers a framework for stakeholders—from engineers to strategists—to harness the promise of edge computing while navigating its complexities in high-stakes applications.