Multi-class classification for machine learning is a key research area that extends binary classification to problems involving more than two classes, with applications in image recognition, natural language processing, bioinformatics, IoT security, and recommender systems. Research papers in this domain explore algorithms such as decision trees, support vector machines (SVM) with one-vs-one or one-vs-all strategies, k-nearest neighbors (KNN), logistic regression, naïve Bayes, and ensemble approaches, as well as deep learning models like CNNs, RNNs, and transformers for handling high-dimensional and complex data. Key contributions include feature engineering, dimensionality reduction, handling imbalanced datasets, and improving interpretability in multi-class settings. Recent studies also focus on scalable frameworks for big data, online learning for streaming data, edge/fog-assisted real-time classification, and hybrid models that integrate statistical, optimization, and deep learning techniques. By advancing multi-class classification, research in this area aims to achieve accurate, efficient, and robust decision-making across diverse real-world applications.