The influence of machine learning (ML) technologies on governance effectiveness and environmental management in rural areas was explored in this study. The role of ML and sensor data in enhancing rural environmental governance was investigated, focusing on its impact on decision-making processes and sustainable outcomes. ML is a data-driven method dependent on continuous, real-time input from distributed environmental sensors. Sensor data, collected through wireless sensor networks and remote sensing platforms, are foundational for the high-resolution, multi-modal data (e.g., air quality, water flow, and soil composition) necessary for ML models to conduct predictive modeling and anomaly detection, thereby transforming traditional governance into a proactive system. To understand how ML with sensor data contributes to the development of environmental governance in rural areas, a questionnaire survey was conducted with 101 participants, and the data were analyzed using the Statistical Package for the Social Sciences. ML was used to process the data and identify environmental factors. A positive perception of ML’s effectiveness in governance was observed. However, correlations between ML effectiveness and other variables were not significant. The resuslts of analysis of variance showed no significant relationship among technological infrastructure, stakeholder engagement, and perceived effectiveness. While ML based on sensor data holds promise for improving rural governance, its integration must be accompanied by robust infrastructure for tangible environmental benefits. Such results enable an understanding of the potential of ML in rural governance and highlight the need for strategic implementation strategies.