This study develops advanced machine learning methods for intelligent industrial systems, addressing single-view imbalanced binary classification, multi-view long-tail multiclass classification, and incomplete multi-view unsupervised clustering. By leveraging cost-sensitive learning, contrastive learning, and knowledge distillation, it enhances robust representation learning. These techniques aim to improve predictive maintenance, fault detection, and process optimization, offering comprehensive solutions for real-world industrial challenges.