The integration of Machine Learning (ML) and the Internet of Things (IoT) is revolutionizing the domains of autonomous vehicles and Supervisory Control and Data Acquisition (SCADA) automation systems. These cutting-edge technologies synergize to address complex challenges, including real-time decision-making, predictive maintenance, and operational efficiency, thereby transforming industries reliant on automation. Autonomous vehicles, empowered by IoT sensors and ML algorithms, achieve enhanced situational awareness, seamless navigation, and adaptive decision-making capabilities. IoT-enabled devices provide continuous streams of data from vehicular environments, while ML processes these datasets to predict potential obstacles, optimize routes, and enhance safety. Similarly, SCADA systems leverage IoT and ML to improve monitoring, control, and fault detection in critical infrastructure such as energy, water management, and industrial processes. IoT integration enables SCADA systems to collect vast amounts of operational data, while ML models analyse these datasets to predict system failures, optimize resource allocation, and enhance operational resilience. The convergence of these technologies not only automates processes but also ensures higher accuracy, scalability, and cost-efficiency. However, the deployment of IoT and ML in these domains raises concerns about cybersecurity and data privacy. SCADA systems and autonomous vehicles are particularly vulnerable to cyber threats, requiring robust security frameworks. Addressing these challenges is essential to fully harness the potential of IoT-ML integration. This paper explores the transformative role of IoT and ML in advancing self-driving cars and SCADA systems, highlighting innovations, challenges, and future directions for achieving sustainable and secure automation.