Preventing cyber-attacks like DoS, Probe, R2I, and U2R intrusions in a digitally interconnected world is very essential. This paper proposes a Real-Time Intrusion Detection System (RIDS) that identifies known and unknown intrusions using AI and ML. The system uses supervised and unsupervised ML algorithms, along with advanced preprocessing and feature selection, to enhance real-time anomaly detection and minimize false positives. It combines conventional and deep learning techniques with the Random Forest (RF) methodology to achieve higher accuracy and improve scalability. Experimental evaluations conducted on standard datasets show the effectiveness of intrusion detection while simultaneously reducing computational requirements.