Enhancing Cyber Threat Intelligence (CTI) methodologies is crucial to secure digital ecosystems in light of increasing cyber threats. This study presents a state-of-the-art method that utilizes Artificial Intelligence (AI) and Machine Learning (ML) techniques in an ensemble framework. The study concentrates on using the UNSW-NB15 dataset, a well-recognized standard in network security research, to train and assess the new model. Our approach centers around combining three robust algorithms: Support Vector Machines (SVM), Random Forest, and Multilayer Perceptron (MLP). We aim to enhance our cyber threat detection system by merging these unique models to capitalize on their complementary advantages, resulting in a more resilient and thorough system. The UNSW-NB15 dataset is an excellent test environment because it accurately represents a wide range of network traffic, including different cyber threats and attacks. The ensemble method we used shows an impressive accuracy of 98.6%, highlighting the effectiveness of the model in detecting and addressing cyber threats in various situations. Our research holds significance beyond just the level of accuracy we have achieved. Combining Support Vector Machines (SVM), Random Forest, and Multilayer Perceptron (MLP) improves detection accuracy and provides a deeper insight into various threat environments. By adopting a holistic approach to Cyber Threat Intelligence, organizations can effectively anticipate and address emerging threats, thereby reducing potential risks. This study adds to the expanding knowledge base on cyber threat detection and mitigation strategies, highlighting the effectiveness of combining AI and ML techniques. The results lay the groundwork for future progress in Cyber Threat Intelligence (CTI), promoting a stronger cybersecurity environment in the constantly changing digital age.