In today’s world of connected systems, protecting those systems is imperative. Intrusion Detection Systems (IDS) have an important function to perform to protect these systems by detecting and monitoring malicious activity and policy breaches. This project develops an intelligent IDS based on Artificial Intelligence (AI) and Machine Learning (ML) to identify potential security threats online. The system applies sound ML methods to examine the traffic on a system and separate benign and malicious behavior. A variety of ML methods / Algorithms exist to differentiate bad and good data, and among those methods are supervised and unsupervised learning models to enhance known and unknown threat detection. Benchmark data sets are used to train the system so that it can detect a variety of attacks, including distributed denial of service (DDoS), probing, and remote-to-local (R2L) attacks efficiently. There is also use of feature selection and dimensionality reduction methods to achieve maximum computational performance and efficiency. The suggested AI and ML-based IDS provides scalable and flexible security through continuous learning to respond to emerging threats. It is a potent choice for securing networks of today because it supports scalability and real-time analytics.