The ecology of Karnataka is seriously threatened by wildlife poaching, which puts endangered animals like elephants, tigers, and leopards in jeopardy. Due to resource limitations and the unpredictability of poaching activities, traditional surveillance techniques like ranger patrols frequently prove insufficient. In order to identify high-risk poaching areas, this research proposes a machine learning method based on Long Short-Term Memory (LSTM) that examines data on human activity, environmental variables, and animal movement patterns. The suggested approach outperforms conventional techniques like manual patrol planning, which normally attain an accuracy of about 65%, with an overall forecast accuracy of 87.5%. The technique improves ranger patrol efficiency by precisely identifying poaching hotspots, allowing for more efficient resource allocation and faster reaction times. To further enhance conservation efforts, future research will concentrate on adding more environmental indicators and putting real-time monitoring into practice.