Abstract Mutation testing is a significant software testing approach that identifies the faults present in the source codes and the potentiality of the test cases in detecting the mutated codes. In this testing approach, equivalent mutant detection is complicated as the test cases will not identify the mutated codes from the source program. To overcome this problem, the article proposes a novel hybrid strategy known as the hybrid wavelet convolutional rain optimization (HWCRO) to classify the equivalent mutants present in the source codes accurately. The proposed technique considers three different classes of equivalent mutants based on the RIPR model and exactly identifies the mutated code. Initially, the features such as the semantic similarity and the information entropy are extracted, and these features are given as the input to the wavelet convolutional neural network (wCNN) classifier. The dimensions of the features are reduced in the convolutional layers using the wavelet function, which enhances the classifier's performance. To improve the classification accuracy, the loss function is minimized with an adaptive rain optimization algorithm (ROA) that iteratively tunes the parameter of wCNN. The proposed approach is compared with the existing classification techniques based on the parameters such as precision, recall, f1‐score, and accuracy, and the simulation results yielded 85.17% accuracy value for the proposed approach.