Protein subcellular localization prediction is crucial for understanding biological functions and cellular processes. This review summarizes recent tools for predicting subcellular localization in eukaryotic, prokaryotic, and viral proteins. The paper discusses various prediction methods, including sequence-based, annotation-based, hybrid, and meta-predictors. It highlights the advantages and limitations of each approach, emphasizing the importance of accurate and efficient prediction tools. The review also presents a detailed taxonomy of these tools, categorizing them based on their features, algorithms, and prediction techniques. It identifies current research gaps and challenges, such as the need for better handling of unannotated proteins and the limitations of homology-based methods. The paper also provides a comprehensive overview of recent prediction tools, including their performance, accuracy, and applicability. It discusses the challenges in subcellular localization prediction, such as data quality, imbalanced datasets, and the complexity of protein sorting. The review concludes that while there are many challenges, significant progress has been made in developing accurate and efficient prediction tools. The paper also highlights the importance of integrating multiple prediction methods to improve accuracy and reliability. Overall, the review provides a detailed analysis of the latest tools and methods for subcellular localization prediction, offering a guide for researchers to select appropriate tools for their needs.Protein subcellular localization prediction is crucial for understanding biological functions and cellular processes. This review summarizes recent tools for predicting subcellular localization in eukaryotic, prokaryotic, and viral proteins. The paper discusses various prediction methods, including sequence-based, annotation-based, hybrid, and meta-predictors. It highlights the advantages and limitations of each approach, emphasizing the importance of accurate and efficient prediction tools. The review also presents a detailed taxonomy of these tools, categorizing them based on their features, algorithms, and prediction techniques. It identifies current research gaps and challenges, such as the need for better handling of unannotated proteins and the limitations of homology-based methods. The paper also provides a comprehensive overview of recent prediction tools, including their performance, accuracy, and applicability. It discusses the challenges in subcellular localization prediction, such as data quality, imbalanced datasets, and the complexity of protein sorting. The review concludes that while there are many challenges, significant progress has been made in developing accurate and efficient prediction tools. The paper also highlights the importance of integrating multiple prediction methods to improve accuracy and reliability. Overall, the review provides a detailed analysis of the latest tools and methods for subcellular localization prediction, offering a guide for researchers to select appropriate tools for their needs.