In recent years, there has been an enormous increase in the volume of malware generation and the classification of malware samples plays a crucial role in building and maintaining security. Hence, there is a need to explore new approaches to overcome the limitations of malware classification such as pre-combustion, peculiarity eradication, and categorization. To overcome these issues, this paper proposes a novel Conglomerate Stratum Model (CSM), which categorizes them into groups and identifies their respective families based on their behavior. Initially, the precombustion process used Triad Seeped Technique (TST) in which the image is first regularized by applying ripples. Secondly, we introduced a Quatrain Layer Method (QLM) to upgrade the robustness of malware image features in peculiarity eradication. Then the specific output of the quatrain layer is given to Acclimatized Patronage Scheme (APS) for categorization, and this process effectively classifies the malware types with greater accuracy. The results demonstrate that our model can achieve 99.41% accuracy in classifying malware samples. Also, the values of sensitivity, precision, negative predictive, and recall are higher than 0.9 with the false-negative rate of 0.04, and the false-positive rate 0.003 proving the model to be optimistic. The experimental comparison demonstrates its superior performance concerning state-of-the-art techniques.