Because of its decentralized, transparent, and secure characteristics, blockchain technology is becoming more and more popular. Therefore, it is imperative to make sure that it is resilient to network threat, particularly distributed denial-of-service (DDoS) assaults. This study tackles the susceptibility of the blockchain system to denial-of-service attacks (DDoS) that compromise its fundamental decentralized features and jeopardize their dependability and security. We have developed a new adaptive integration method for identifying and detecting different types of DDoS attacks. In order to verify the resilience and accuracy of our methodology, we extracted a dataset that combined several DDoS attacks. This allowed us to use our methodology to identify DDoS threats and then divide them into seven distinct assault subcategories. A comprehensive framework that smoothly integrates a hybrid sine–cosine chimp optimization model has been suggested to address the wide range of variances in DDoS attacks. Our framework’s novel feature is addition of the dynamic weight adjustment method, which improves system flexibility. Empirical findings validate the effectiveness of our ensemble approach over singular models for a range of assessment criteria. With rates for classification tasks and 99.56% for detection, the framework demonstrated impressive accuracy. This work opens the door for enhancing blockchain systems’ security mechanisms against DDoS attacks by creating an all-encompassing and flexible methodology. The dynamic weight adjustment in conjunction with the ensemble technique shows promise in guaranteeing the long-term security and reliability of blockchain technology.