Malware attacks pose a significant threat to the security of computer systems and networks. Traditional signature-based methods are becoming less effective as malware evolves in complexity and sophistication. Traditional methods of malware detection, relying heavily on signature-based approaches, struggle to keep pace with the dynamic and polymorphic nature of modern-day malware. As a consequence, there is a pressing need for innovative and adaptive solutions that can effectively identify and mitigate both known and emerging malware threats. This research proposal addresses the aforementioned challenges by advocating for a paradigm shift in malware detection through the integration of ML and deep learning DL techniques. This paper proposes a novel approach to enhance the accuracy and robustness of malware detection by combining the strengths of a conventional ML classifier and an ANN classifier. The proposed model was developed in a two-step process. Firstly, a ML classifier was employed to capture inherent patterns in the data, and secondly, a neural network was utilized to discern complex relationships. The predictions from both models were then integrated into a meta-model, demonstrating an effective fusion of rule-based and learned features. For ML classifiers, five classifiers, namely, SVM, KNN, Decision Tree, Random Forest, and Ada boost are used. Each of the classifiers is combined with an ANN classifier, and the best combination is used for malware classification. Experimental results showcased the potential of this combined approach to improve accuracy and adaptability in the detection of malware.