As the pace of technological advancement accelerates; our increasing reliance on internet-based services for everyday tasks has become both a hallmark of modern society and a growing source of concern. This widespread dependence on online platforms not only enhances convenience but also magnifies our exposure to a broad array of cyber threats. The expanded digital landscape introduces a host of security vulnerabilities; making robust cybersecurity measures a critical necessity. Among these, the demand for sophisticated antivirus software and similar security solutions has surged, with a particular emphasis on systems capable of proactively identifying and neutralizing emerging threats. A highly effective response to these challenges lies in the implementation of a specialized system designed to safeguard network infrastructures against various forms of unauthorized access and malicious activity. This system, known as an intrusion detection system (IDS), serves as a critical line of defense by continuously monitoring network traffic to detect any deviations from normal behavior that may indicate an intrusion. The IDS plays a crucial role in securing network environments by leveraging advanced detection techniques to identify potential threats and respond to them before significant damage can occur. This study focuses on the design and analysis of an IDS enhanced by the integration of machine learning and neural network (ML-NN) technologies. The proposed system is meticulously crafted to achieve superior performance in identifying and classifying a wide range of cyberattacks, as demonstrated by its application to the CIS IDS dataset. Through rigorous experimentation, the research explores both binary and multiclass classification tasks, employing key performance indicators such as precision, accuracy, and recall to assess the system's effectiveness. The ML-NN-based IDS framework exhibits a remarkable ability to discern and categorize various attack vectors, outperforming existing methodologies. The experimental findings reveal that the proposed IDS framework achieves a precision of 99.87%, a recall of 99.89%, and an overall accuracy of 99.0%. These results represent a significant enhancement, with an accuracy improvement of 1.07% compared to previous approaches. The study not only highlights the effectiveness of the ML-NN approach in detecting and mitigating cyber threats but also opens avenues for further research, particularly in refining detection algorithms and expanding the scope of attack classifications.