Rice stem borers are among the most destructive pests of rice, causing significant yield losses due to concealed larval feeding within plant stems. Early detection of infestation is difficult using conventional monitoring techniques such as visual scouting or pheromone traps, which often identify the problem only after considerable damage has occurred. This study presents a conceptual framework for an Internet of Things (IoT) and machine learning (ML) enabled system for early and non-invasive detection of rice stem borer activity. The proposed system integrates acoustic and vibration sensors with low-power IoT hardware, wireless communication modules, cloud-based analytics and ML-driven classification models. Micro-acoustic and vibrational signals generated during larval feeding are captured in real time and processed using signal-processing techniques such as filtering, spectral analysis and feature extraction. These processed signals are subsequently analyzed using machine learning models to detect infestation patterns and generate early warning alerts. The architecture includes field sensor nodes installed near rice stems, an IoT gateway for data aggregation and transmission, cloud-based processing units and a farmeroriened mobile interface. This integrated approach enables continuous monitoring of pest activity under field conditions. The system also supports precision pest management by enabling timely intervention and reducing unnecessary pesticide application. The proposed framework highlights the potential of combining modern sensing technologies with intelligent analytics to improve agricultural sustainability and crop protection. The study also discusses challenges related to field deployment, environmental noise, data availability and system scalability. Overall, the integration of acoustic sensing, IoT communication and machine learning offers a promising pathway for developing smart pest surveillance systems in rice production ecosystems.