In the fast-developing information technology, research, and especially cyberspace, malicious detection and identification will be very essential as they will help in saving various systems and data. Today, we are forced to make a sad conclusion that the classical measures of security are collectively unable to fight against the advanced cyber threats, thereby illustrating the significance and value of an introduction of the artificial intelligence, such as deep learning, into the malware detection domain. In this article, a fuzzy RNN-based deep learning model is proposed which is a combination of efficient malware detection and classifying. The objective is achieved by using Malicious Sequential Pattern Extraction (MSPE), which is then implemented into the Deep Learning (DL) model to lift up the corresponding model accuracy and capability to identify malware. Further, the architecture design incorporates an application of fuzzy logic, with an aim to improve the system performance by offering the system the capability for tailoring and making a difference between good and evil software. We present a comparison of our suggested deep learning architecture with traditional machine learning methods (like Support Vector Machine (SVM), Logistic Regression, and Random Forest) that are part of standard security systems. As the central component of our research, the results suggest that the RNN-based deep learning approach along with SPME and fuzzy system seems to be the best method to achieve accurate detection and malware classification compared to conventional models. This study does not only underline the efficacy of using deep learning to overcome the threats posed today by the malware but, furthermore, displays the blossoming of sequential pattern mapping and fuzzy logic integration in fighting the cybersecurity battles of the modern age.