This study introduces a novel wireless sensory system for automated monitoring of construction machinery, leveraging audio and kinematic data to enhance job site operations, including scheduling, cost forecasting, safety assessments, and fleet optimization. The system features two embedded units based on ARM processor architecture: a sensor unit integrating a microphone, accelerometer, gyroscope, and radio module, capable of transmitting data up to 100 feet, and a receiver unit for data collection and processing. This design enables flexible, real-time monitoring, overcoming the limitations of traditional wired systems. The accuracy of the hardware was validated through extensive field tests conducted at three different distances: 30, 60, and 100 feet. The results obtained from these tests were promising, indicating high reliability and effectiveness. In particular, the system’s multi-modal approach, which combines audio and kinematic data, demonstrated an average accuracy of 79.46% in recognizing construction machinery activities. This significantly outperforms models that rely solely on single data types and underscores the efficacy of integrating diverse data sources for precise activity recognition. Such accuracy is vital for improving operational efficiency and ensuring safety at construction sites. However, the research also underscores the need to optimize sensor placement and data transmission, particularly over longer distances, to enhance system performance further. These results offer significant insights into the development of advanced, automated monitoring systems in the construction industry, marking a step forward in the field’s ongoing digitalization and innovation.