This paper presents NetSentinel, a machine learning-based intrusion detection framework designed to identify malicious network traffic with high accuracy. The system utilizes a Random Forest classifier trained on the NSL-KDD dataset using an 80:20 train-test split. The proposed architecture integrates a Python-based machine learning pipeline with a Node.js backend and MongoDB database for efficient data handling and user interaction. Performance evaluation using metrics such as accuracy, precision, recall, and F1-score demonstrates strong detection capability. Additionally, confusion matrix and ROC curve analysis validate the robustness of the model. The system aims to provide a scalable, efficient, and reliable cybersecurity solution. Future enhancements include real-time traffic capture, streaming integration, and deployment in live network environments.