Cloud computing is a dynamic part of today’s high-tech framework, given that frequent welfares such as cost-effectiveness, scalability, convenience, novelty, and safety. Its impact is multifaceted, transforming competition and corporate operations in the digital age. To improve speed, optimize resource usage, and support sophisticated applications, cloud computing makes use of a variety of learning strategies. A learning technique’s effectiveness in the field of cloud security depends on its ability to recognize, stop, and handle security threats. In order to identify and reduce security threats, machine learning particularly anomaly detection using supervised and unsupervised learning is crucial with advancement of federated learning. Deep learning models like RNNs and CNNs process extensive datasets to uncover intricate attack patterns, while federated learning improves privacy by training models on decentralized data sources. Reinforcement learning facilitates adaptive security strategies, continually enhancing threat responses. Security is paramount in cloud computing as it safeguards sensitive data, applications, and services hosted on cloud platforms from unauthorized access, breaches, and cyber threats. This paper highlights the security concerns in cloud environment with framework to improve the performance matrix to recognize federated cloud trust.