Abstract—Forest monitoring has become increasingly difficult as pressures from land-use change, climate variability, and illegal activities continue to grow. Much of the existing monitoring still depends on field surveys and manual interpretation, which are costly and hard to maintain over large or remote forest areas. Because of these limitations, artificial intelligence has been explored in recent years as a supporting tool for forest conservation. This paper brings together recent studies that use Artificial Intelligence (AI) for forest-related applications. The work reviewed includes satellite- and UAV-based change detection, wildlife monitoring using camera traps and acoustic sensors, and predictive models used to assess deforestation, fire risk, and illegal activities. The article also explains how these approaches are integrated with sensor networks, edge computing, and cloud platforms. Looking into the reviewed studies, it has been identified that AI is employed to automate the analysis of the collected information as well as to manage large amounts of ecological information. Even though the performance of AI has been enhanced compared to the existing methods, there are still challenges that have not been addressed. These include the reliance on regional datasets, the lack of evaluation of AI performance in realistic deployment settings, and the restrictions of real-world infrastructures. The ethical considerations have been addressed, but there is no uniform treatment of these issues. Overall, the current literature suggests a complementary role of AI technology within a comprehensive forest governance system.

Index Terms—artificial intelligence, conservation decision support, deforestation prediction, forest monitoring, IoT and edge computing, remote sensing, wildlife and biodiversity assessment

Cite: Maddhigalla Lakshumaiah and D. S. Rao, "Overview of AI-Enabled Forest Monitoring and Conservation Framework," International Journal of Electrical and Electronic Engineering & Telecommunications, vol. 15, no. 4, pp. 276-287, 2026.  doi: 10.18178/ijeetc.15.4.276-287

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).