The adoption of cloud computing in the financial and retail sectors has significantly enhanced scalability, data accessibility, and operational efficiency. However, the increased reliance on cloud-based infrastructure has also led to heightened cybersecurity threats, including unauthorized access, data breaches, and system vulnerabilities. Traditional security mechanisms often fail to address these evolving risks due to their inability to process large-scale, dynamic threats in real time. To overcome these challenges, this study proposes an AI and ML-powered cybersecurity framework that integrates predictive analytics and anomaly detection to safeguard cloud environments. Six machine learning models, including Linear Regression, SVR, Decision Tree, KNN, Random Forest, and XGBoost, were evaluated for their ability to detect security threats and forecast demand fluctuations. The results indicate that XGBoost achieved the highest R2 score (0.9969) and the lowest RMSE (8.21), outperforming traditional models in both accuracy and robustness. Feature importance analysis revealed that network traffic logs, API access patterns, and authentication anomalies were the most influential indicators of security risks. The findings validate that AI-driven cybersecurity strategies enhance cloud security, improve anomaly detection, and enable real-time risk mitigation, making them highly effective for securing financial and retail cloud infrastructures.