Sustainable forest management faces challenges owing to climate change and biodiversity loss, necessitating the adoption of advanced real-time monitoring technologies. In this study, we evaluated the performance of an IoT sensor network comprising 20 nodes deployed over 180 days across four forest types in Hebei Province, China. Following long-range wide-area network communication protocols, the system transmitted 86400 environmental observations, achieving a cumulative data completeness rate of 96% across the 180-day deployment period, demonstrating high technical resilience despite gradual battery depletion. Quantitative analysis results revealed substantial microclimatic variability among forest types: pine forests exhibited the highest mean temperature (28.71 ± 4.82 °C), eucalyptus forests recorded the highest mean humidity (67.23 ± 7.67%), and indigenous forests demonstrated superior soil moisture retention (13.64 ± 7.33%). Through the simultaneous assessment of technical performance, a mean daily battery depletion rate of 0.56–0.58% was determined and 521 detection events were identified in wildlife monitoring. By integrating environmental observations with deep-learning-based species recognition, such results provide an effective sensing framework for real-time sustainable forestry management. Technical performance assessments indicated a mean daily battery depletion rate of 0.56–0.58%, with power consumption significantly affected by environmental conditions. Wildlife monitoring yielded 521 detection events encompassing 2134 individual animals across six species, with an average identification confidence score of 0.849. The system’s multimodal data framework, which integrates soil dielectric permittivity, semiconductor-based thermal sensing, and machine-learning-based species recognition, proved effective in supporting ecological surveillance. Overall, the IoT network offers a scalable digital infrastructure for proactive forest conservation.