J. Sravanthi, Polsani Spoorthi, Tharala Raghavi, Shaik Asif, Burra Sai Vinay 03 03 Technological advancements have fundamentally transformed modern defense operations particularly within surveillance reconnaissance and battlefield decision support systems While military environments generate vast volumes of visual data from satellites and unmanned aerial vehicles UAVs conventional manual interpretation remains resourceintensive and prone to human error hindering realtime operational responses To address these systemic bottlenecks this research introduces an intelligent highassurance decision support framework for the automated classification of tactical military imagery The proposed system transitions beyond traditional rulebased software by integrating a suite of soft computing models including Perceptron Decision Tree Classifiers DTC and Deep Neural Networks DNN Central to the framework is a novel Hybrid Convolutional Recurrent Model CRM which synergizes Convolutional Neural Networks CNN for spatial feature extraction with Long ShortTerm Memory LSTM networks to capture essential temporal dependencies in dynamic battlefield scenarios The architecture is encapsulated within a modular graphical interface designed for streamlined data ingestion model training and performance visualization Experimental validation demonstrates that the integrated CRM significantly enhances processing speed and classification reliability providing a scalable and robust technological solution for modern military intelligence and tactical decisionmaking Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026