Classification algorithms for machine learning are a core research area aimed at developing models that can accurately categorize data into predefined classes, with applications across image recognition, text mining, medical diagnosis, IoT security, and fraud detection. Research papers in this domain explore traditional algorithms such as decision trees, naïve Bayes, k-nearest neighbors (KNN), logistic regression, and support vector machines (SVM), as well as advanced methods including ensemble approaches (random forests, boosting) and deep learning architectures like CNNs and RNNs. Key contributions include feature selection, dimensionality reduction, handling imbalanced datasets, and improving model interpretability and generalization. Recent studies also focus on scalable classification frameworks for big data, real-time classification using edge/fog computing, and hybrid models that combine statistical and deep learning methods. By leveraging classification algorithms, research in this area aims to provide accurate, efficient, and adaptable solutions for diverse and complex real-world problems