With cloud computing, the issue of guaranteeing the security of such cloud infrastructures has gained an urgent need because of threats which are growing in numbers including unauthorized access, data breaches, and even distributed denial-of-service (DDoS) attacks. This study introduces a novel hybrid Artificial Intelligence (AI) system with Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Deep Neural Network (DNN) base models that can be used to preemptively identify and tackle cyber threats in the cloud. The correctness of the proposed hybrid model was 98.67% with precision of 97–82%, recall of 98–21% and F1-score of 98–01, which is much superior to the performance of individual AI models and traditional rule-based systems. Lower false positives and better zero-day threats adaptation were also proven by comparative experiments. Due to the combination of machine learning and the deep learning models, the detection capacity expanded together with the system durability or stability; thus, the technology is appropriate in terms of the real-time implementation in the dynamic cloud environment. The findings show that the hybrid AI methods promise to turn cloud security into a proactive system that is more reliable, with automated threat elimination mechanisms.