In the ever-evolving landscape of digital threats, the necessity towards a robust Network Intrusion Detection System (NIDS) progressed into paramount. The envisioned system aims to transcend traditional limitations by dynamically adapting to emerging threats through continuous learning and behavioural profiling of normal network activities. A novel approach was introduced for NIDS by combining various machine learning algorithms resulting in creation of a hybrid model. By leveraging multiple ML algorithms, namely Decision tree, K-nearest neighbour and Logistic regression it results in the formation of a robust and adaptive hybrid model that can efficiently identify and reduce the network threats. An experimental evaluation is conducted using benchmark datasets and real-world network traffic scenarios, the proposed hybrid model exhibits better performance of about 94% contrasted with state-of-art algorithms and also the system achieves low false positives and false negatives. This contributes to network development by providing a new and effective intrusion detection method. The ability of the hybrid model to combine different algorithms not only increases detection accuracy but also provides versatile framework optimization.