The new cloud infrastructures have become very complex and large, demanding the use of proactive security monitoring mechanisms to detect threats and provide recommendations on how to act upon them emerging risks. Currently, most existing cloud forensics frameworks operate only reactively, do not integrate sources of data of varied types, and fail to produce alerts in real-time, context-oriented and interpretable formats. Also, most traditional models lack a federated form of adaptability and probabilistic validation, rendering them less effective and scalable in actual operating conditions around the globe. This paper throws light upon a well-built, comprehensive Recommendation-Based Cloud Forensics Framework for pre-emptive detection of security events through an integration of five completely new analytical methodologies. The first is called Multi-Source Dynamic Risk Vector Embedding (MS-DRVE) and receives heterogeneous data sets like logs, traffic, and user behavior in one time-risk vector entry via attention-based encoding. The Graph Convolutional Markov Decision Networks (GCM-DNet) have shown how their creation enables indeed real-time alerting through modelling the threat propagation among the cloud entities as a Markov process on dynamically emerging graphs. Third, Explainable Multi-Modal Transformer (X-MMTrans) accommodates direct and interpretable visualizations of anomaly trajectories and system behaviors across multi-modal embeddings.. Fourth, such as Federated Adaptive Recommendation Engine with Contrastive Learning (FARE-CL), allows a decentralized learning, personalized, privacy-preserving security recommendations across distributed cloud nodes. Finally, the Bayesian Evidence Accumulation Framework (Bay EVAL), involves a probabilized, time-aware evaluation mechanism for the reliability and effectiveness validation of the proposed system sets. The precision attained with this proposed framework is high (94%); with low false positive rates (<3.5%); and improved interpretability, thus enhancing threat mitigation in advance, decision-making efficiency, and deployment confidence on the cloud security operations. Such work paves the way toward-generation intelligent and explainable cloud forensic systems.