It is essential and unavoidable to detect Malware on the Internet, as a wide range of online IT services are available. Portable Executable files are the most frequently targeted platform by Malware. Malware must be promptly identified and alerted in a real-world environment by establishing a deployable learning system. The researchers applied machine learning to a Malware dataset, observing the model's performance metrics at a high computational cost, but were unable to deploy the model in a real-world environment. A deployable machine learning model using RF, attaining an accuracy of 97.16%, precision of 95.21%, and F1 score of 95.24% is achieved in the proposed research work, which is particularly adept at accurately identifying Malware. We have developed a novel classification model that employs the Support Vector Machine (SVM) to classify preprocessed data, detecting malware and normal instances. Furthermore, the SHAP technique identifies significant features, including SizeOfStackReserve, DllCharacteristics, and MajorImageVersion. The use of SHAP values facilitates an understanding of the characteristics of each feature in the model's prediction. Employing the SHAP algorithm using the trained SVM model to reduce the features, attained an accuracy of 97.16%.