This research presents a detailed study on the development of an IoT-integrated ultrasonic cleaning system designed to enhance efficiency, adaptability, and sustainability in cleaning applications. Ultrasonic cleaning, widely used in medical, electronics, and industrial manufacturing, relies on high-frequency sound waves to generate cavitation bubbles, which effectively remove contaminants from delicate surfaces. However, traditional ultrasonic cleaners operate on fixed parameters, leading to inefficiencies, high energy consumption, and lack of real-time adaptability. The integration of IoT (Internet of Things) allows for real-time monitoring, adaptive cleaning cycle optimization, and predictive maintenance, which significantly improves cleaning performance, reduces energy waste, and extends equipment lifespan. The proposed system features a 10-liter stainless steel tank, a 200W ultrasonic transducer array, and a smart IoT-driven monitoring system. Using temperature, turbidity, and conductivity sensors, the system dynamically adjusts ultrasonic frequency, cleaning duration, and power output based on contamination levels and material sensitivity. Experimental results demonstrate that the IoT-based ultrasonic cleaner achieves up to 95% cleaning efficiency, with 25% energy savings and 30% reduction in water usage compared to traditional ultrasonic cleaning systems. The study also explores real-time data logging, predictive fault detection, and remote-control capabilities using ESP32-based IoT integration. This novel approach significantly enhances industrial automation and sustainability, paving the way for next-generation ultrasonic cleaning technologies in healthcare, semiconductor manufacturing, and precision engineering. Future research will focus on AI-driven automation, deep learning-based fault detection, and integration of advanced cavitation modeling to further optimize ultrasonic cleaning efficiency in large-scale industrial applications.