Illegal logging and poaching in forests threaten the forest ecosystem and cause biodiversity loss and environmental degradation. However, traditional monitoring methods lack scalability and real-time responsiveness. To overcome these limitations, we developed a long-range (LoRa)-based environmental sound classification alert system for real-time forest surveillance and alerting. With low-power hardware components such as Raspberry Pi, Arduino, and Dragino LoRa modules, the developed alert system captured and transmitted audio data. It also converted and compressed sound signals into Mel spectrograms using dimensionality reduction for the low-bandwidth LoRa network. A machine learning model then classified the sounds into “dangerous” (e.g., chainsaws and gunshots) and “safe” categories. The system showed an accuracy higher than 85% in detecting threats and presented reliable performance under noisy field conditions in the experiment. The alert system can operate efficiently in large forested areas with minimal maintenance. While challenges remain such as limited transmission capacity and occasional misclassifications, it is necessary to explore convolutional neural network-based models, edge AI integration, and deployment at scale. The developed forest alert system enables scalable and cost-effective IoT-AI solutions for forest protection and supports data-driven sustainable ecosystem management.