The swift advancement of the Internet of Things from a theoretical concept to practical reality has deeply affected numerous sectors, including process control systems. This paper extensively explores the dynamic landscape of IoT technologies, particularly their integration into streaming data analytics for process control applications. By seamlessly integrating crucial components such as sensors, connectivity protocols, cloud computing, and artificial intelligence, IoT facilitates efficient device connectivity and data exchange, fundamentally revolutionizing traditional process control methodologies. Leveraging machine learning techniques within IoT-driven streaming data analytics empowers industries with real-time decision-making capabilities, predictive maintenance strategies, and optimized industrial processes, thereby extracting actionable insights for enhanced operational efficiency and cost savings. Despite encountering challenges like data security concerns and interoperability issues, continuous advancements in connectivity, edge computing, and sensor technologies promise to address these obstacles and fully unleash the potential of IoT-driven streaming data analytics in process control systems. Looking forward, emerging trends in this domain include the adoption of smart infrastructure, autonomous vehicles, wearable healthcare devices, and immersive augmented reality experiences, indicating a future characterized by unparalleled connectivity and innovation in process control. In conclusion, the convergence of IoT and ML in streaming data analytics signifies a transformative shift, enabling industries to achieve heightened efficiency, elevate product quality, and tackle global challenges, thereby laying the foundation for a future that is more sustainable and resilient.