The work “Tracing Footprints of Anti-Forensics and Ensuring Secured Data Transmission in the Cloud Using an Effective ECCDH and Kalman Filter” addresses the critical challenge of thwarting anti-forensic operations to protect data integrity in the context of cloud computing. This research digs into the complex difficulties encountered by digital forensics in multi-tenant virtual infrastructures in the context of cloud technology vulnerabilities and privacy issues inside cloud settings. The inquiry covers both technical and legal ground, including the examination of evidentiary photos and residual data. The complexity of forensic attempts is amplified by the convergence of diverse settings, the widespread use of privacy, encryption, and anti-forensic measures, and the intricate architecture of Cloud Service Providers (CSPs). Because of its decentralized nature, cloud computing exacerbates problems caused by quickly growing databases. Anti-forensic assaults are a serious concern within the realm of cloud security risks because they obfuscate attack evidence throughout the whole forensic investigation process. The research presents a novel method of combating these evasive strategies by combining ECCDH (Elliptic Curve Cryptography Diffie-Hellman) with Kalman Filters. This cutting-edge combination not only successfully identifies anti-forensic traces, but also guarantees private data transfer to the right people. The suggested approach uses the Kalman Filter in conjunction with encryption, decryption, compression, and decompression of data to identify attacked and un-attacked packets. The KDD-Cup dataset is used to conduct empirical validation of the approach. The results of the investigation corroborate the usefulness of the suggested method for enhancing cloud data transmission security and revealing potential anti-forensic actions. In particular, the attained values for accuracy, precision, recall, and F-score are as follows: 98.68, 99.06, 98.68, and 98.68. The proposed method outperforms state-of-the-art methods in terms of accuracy, recall, precision, and F-score, according to a comparison with the latter.