M. Ramana Kumar, Polasa Vaishnavi, Dyagala Varshith, Mohammed Sohail 03 04(1) Contemporary defense systems are increasingly dependent on intelligent technologies to enhance surveillance reconnaissance and strategic decisionmaking capabilities The rapid growth of visual data generated from satellites and unmanned aerial platforms has made manual image analysis and traditional rulebased approaches inefficient laborintensive and unsuitable for largescale processing To overcome these limitations the proposed system employs advanced Machine Learning and Deep Learning techniques to automate the classification of military imagery The framework integrates multiple algorithms including Perceptron Decision Tree Classifier DTC Deep Neural Networks DNN and a Hybrid Convolutional Recurrent Model CRM The CRM combines Convolutional Neural Networks CNN for robust spatial feature extraction with Long ShortTerm Memory LSTM networks to model complex sequential relationships within the extracted features These models are trained on wellstructured military image datasets to accurately categorize assets into predefined strategic classes Experimental results demonstrate that DNN and CRM models deliver improved accuracy and reliability depending on dataset characteristics and training conditions Additionally the system is equipped with a userfriendly graphical interface that allows seamless dataset uploading model training prediction generation and visualization of performance metrics By consolidating multiple models into a single platform and identifying the most effective one for deployment the proposed solution significantly improves the efficiency accuracy and robustness of automated military image analysis thereby enhancing intelligence operations and supporting informed decisionmaking Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026