Securing contemporary computer networks has become increasingly difficult as cyber-attacks continue to grow in complexity and sophistication. Conventional Intrusion Detection Systems (IDS) often fall short in recognizing emerging threats because they depend heavily on predefined attack signatures. To overcome this limitation, hybrid intelligent methodologies that merge clustering with optimization strategies have gained attention as effective tools for improving intrusion detection and classification. This study introduces an enhanced hybrid model that combines K-means clustering with both Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to strengthen anomaly detection and misuse detection within IDS environments. The approach was tested on the KDD CUP 99 dataset, a standard benchmark in intrusion detection research. The developed Hybrid Clustering Algorithm II (HCAII) refines the detection process by lowering false-positive rates and achieving high accuracy across major attack categories, including Denial of Service (DoS), Probe, User-to-Root (U2R), and Remote-to-Local (R2L). Comparative evaluations indicate that HCAII surpasses traditional clustering and optimization methods by offering superior detection performance and more reliable classification outcomes. Overall, the proposed framework addresses critical limitations in existing IDS techniques and provides a resilient, adaptable solution capable of defending network infrastructures against continuously evolving cyber threats.