Molecular imaging (MI) is a science that uses imaging methods to reflect the changes of molecular level in living state and conduct qualitative and quantitative studies on its biological behaviors in imaging. Owing to its high specificity and sensitivity, it has been widely used in preclinical research and clinical surgery. Data processing and analysis are critical to MI. In recent years, artificial intelligence technology, especially neural network-based machine learning technology, has been widely used in MI because of its powerful data processing capability. It provides a feasible strategy to deal with large and complex data for the requirement of MI. In this review, we will focus on the applications of machine learning methods in different modalities of MI.