Cloud computing is a network-based model where the data are accessed by the user anywhere and anytime. Cloud computing is a technology that provides resources and utility services according to user demand. Due to this demand, efficient cloud security methods are highly required, especially at the time of data communication for user authentication. •security is an important task to protect data and cloud resources from harmful activities. Therefore, a novel intrusion detection system is required in cloud networks to detect malicious activity and improve secure data transmission. In this paper, a novel technique called extreme learning based Gestalt Pattern Matched Extreme Learned Cryptographic Blockchain (GPMELCB) model is developed. The GPMELCB model includes three major processes namely user registration, block generation and validation, and secure data communication. First, the Cloud user registration process is performed and generates the user identity and the password. Then the user generates the block with the help of the Davis Mayer cryptographic compression function to generate the hash value of the data and stored it in Blockchain central server. The server performs block validation using Nelder–Mead Byzantine Fault Tolerance consensus algorithm. When the user wants to access the data from the server, the first verify their authenticity using a Gestalt pattern-matched Extreme Learning Classifier