This review discusses recent developments in machine learning (ML) for mass spectrometry (MS), highlighting its growing role in various MS-based applications. ML has gained traction due to advances in computational hardware and the development of new algorithms for artificial neural networks (ANNs) and deep learning. Modern ML methods have enabled new approaches in MS, particularly in areas like mass spectrometry imaging and proteomics. The review provides an overview of ML methodologies relevant to MS, including preprocessing, featurization, supervised and unsupervised learning, ensemble learning, and various types of neural networks such as convolutional, recurrent, and graph-based networks. It also covers applications of ML in MS, including spectral preprocessing, compound identification, sample classification, and omics-based studies. The review emphasizes the importance of ML in overcoming challenges in MS data analysis, such as the need for accurate identification of unknown analytes and the complexity of MS data. It discusses various ML models and their applications in MS, including support vector machines, tree-based models, and deep learning architectures. The review also highlights the potential of ML in improving the accuracy and efficiency of MS-based analyses, particularly in clinical diagnostics and omics research. Overall, the review underscores the synergy between ML and MS, offering insights into the future directions of this emerging field in measurement science.This review discusses recent developments in machine learning (ML) for mass spectrometry (MS), highlighting its growing role in various MS-based applications. ML has gained traction due to advances in computational hardware and the development of new algorithms for artificial neural networks (ANNs) and deep learning. Modern ML methods have enabled new approaches in MS, particularly in areas like mass spectrometry imaging and proteomics. The review provides an overview of ML methodologies relevant to MS, including preprocessing, featurization, supervised and unsupervised learning, ensemble learning, and various types of neural networks such as convolutional, recurrent, and graph-based networks. It also covers applications of ML in MS, including spectral preprocessing, compound identification, sample classification, and omics-based studies. The review emphasizes the importance of ML in overcoming challenges in MS data analysis, such as the need for accurate identification of unknown analytes and the complexity of MS data. It discusses various ML models and their applications in MS, including support vector machines, tree-based models, and deep learning architectures. The review also highlights the potential of ML in improving the accuracy and efficiency of MS-based analyses, particularly in clinical diagnostics and omics research. Overall, the review underscores the synergy between ML and MS, offering insights into the future directions of this emerging field in measurement science.