Credit risk analysis plays a vital role for financial institutions in making informed decisions within today’s increasingly data-centric environment. However, traditional credit risk models often lack the precision and adaptability required to meet modern demands due to constraints in data processing capabilities. This research introduces a cloud-optimized framework that incorporates advanced machine learning methods to enhance credit risk modeling. The proposed approach employs models like XGBoost, LightGBM, and a Hybrid Random Forest- Neural Network, with the hybrid model demonstrating outstanding performance metrics, including an accuracy of 92.8%, a precision of 92.1%, and an ROC-AUC score of 94.2%. Additionally, the cloud-based architecture achieves significant efficiency improvements, reducing training time from 45 min on on-premises systems to 18 min and lowering prediction latency from 50 ms to just 12 ms. The architecture also ensures unlimited storage capacity, seamless scalability, and improved cost efficiency, rising from 65% in traditional setups to 85% on the cloud. These advancements overcome the challenges of traditional systems by enabling real-time data processing, high computational efficiency, and compliance with regulatory standards. This study underscores the potential of cloud-optimized machine learning systems to transform credit risk analysis, delivering a robust, accurate, and scalable solution for financial institutions.