<p>One of the primary concerns that the Indian government is currently addressing is Elephant-Human Encounters (EHE). In this study, we introduced a Multi-Level Long Short-Term Memory (ML-LSTM) framework with control action implementation based on Q-learning. Here, multi-level indicates the stacked LSTM layers for the improved classifier efficiency. This framework is designed to detect the presence of elephants, classify their State-Of-Mind (SOM), and implement appropriate control actions. A microphone is employed to perpetually monitor the sound signals of the environment. Temporal and spectral features specific to elephants are extracted from the sound signal received from the environment. The ML-LSTM framework is then employed to further classify the elephant. Upon ascertaining the SOM, Q-learning-based control actions are implemented to prevent elephant conflict. Accuracy has been observed for a variety of configurations of Mel-Frequency Cepstral Coefficients (MFCC), Delta MFCC (D_M), Double Delta MFCC (DD_M), Linear Predictive Cepstral Coefficients (LPCC), Kurtosis, Skewness, and Linear Predictive Coefficients (LPC). This work experiments with the various combinations of the above-mentioned features. Among that fusion, all the features together gave an improved performance. Further, the findings indicate that the multi-stacked LSTM network outperformed the traditional models. The four distinct elephant SOMs were classified, and three distinct Q-learning-based control actions were implemented. The classification accuracy was 98.91% for the elephant and non-elephant classifications. For state-of-mind detection, the accuracy was 98.75%. This work applied Q-learning-based appropriate control actions that are adaptive to the SOM of the wild animal and environmental conditions, in contrast to conventional direct control mechanisms. For the choice of the right control action using Q-learning, the obtained success rate was 92%.</p>