As cyber threats grow increasingly complex, traditional malware detection systems are getting less capable due to their heavy dependency on the signatory way that threat operates to catch threats. In the research, we will introduce a new domain on which the enough robust darknet will be trained for ML and deep learning-based powerful malware detectors and investigate the degree of performance during the training phase. The innovation of this study lies in the integration between feature extraction and classification strategies from deep neural networks (DNN) with more traditional ML models, such as random forest (RF) and support vector machines (SVM). The proposed system employs a two-tiered approach wherein features are auto-extracted from raw malware samples by a model of convolutional neural networks (CNNs), and subsequent feature extraction is done by the classified approach using machine learning-based classifiers, yielding higher detection accuracy. A robust experiment on the highly diversified dataset of malware indicates that the hybrid system exceeds standalone conventional models in terms of detection rate, false positive rate, and computational efficiency. The predictive model built from the ensemble of tree-based algorithms is based on decision trees, support vector machines, and convolutional neural networks in explaining the accurate identification of malware with benign applications. The processed dataset contains a number of features for an executable file. The peel-off identify malware with benign applications using an ensemble of tree-based algorithms will be divided into a train and test set. These were trained and optimized in models and their performance was evaluated against several metrics, including accuracy, precision, recall, and the F1-score. In the study, we constructed a predictive model to classify malware from benign applications with very high accuracy. As long as the results are a significant improvement compared to the traditional methods, design of a deep learning model outperformed in improving performance of malware detection systems; the research bears broader implications on the field of cybersecurity in general by changing the paradigm that has long been ruled by traditional human-centric analytical techniques. This goes on to show how ML and DL offer a new horizon in bringing cybersecurity operations on par with emerging threats.