Domain adaptation is a crucial challenge in the field of machinery fault diagnosis, as the performance of traditional fault diagnosis models could significantly degrade when applied to different working conditions or domains. This paper proposes a novel approach for domain adaptation for machinery fault diagnosis based on the Critic Classifier Generative Adversarial Network (GAN). Our method aims to improve diagnostic performance by aligning the source and target domains, enabling effective knowledge transfer between them. We leverage the power of the Critic Classifier GAN framework, which incorporates both a generator adversarial network and a critic classifier. This framework enables us to learn representations invariant to domain shifts, leading to accurate classification of fault patterns. Additionally, we employ domain discrepancy loss functions, such as Maximum Mean Discrepancy (MMD) and Maximum Classifier Discrepancy (MCD), to further enhance domain alignment and classifiers to align the feature distributions. Experimental evaluations conducted on various mechanical failure datasets confirm our proposed method’s effectiveness and robustness compared to existing domain adaptation techniques. Our proposed solution effectively overcomes the challenges arising from domain shift and achieves state-of-the-art performance in machinery fault diagnosis under various working conditions.