The application of machine learning (ML) techniques to metal-based nanomaterials has contributed greatly to understanding the interaction of nanoparticles, properties prediction, and new materials discovery. However, the prediction accuracy and efficiency of distinctive ML algorithms differ with different metal-based nanomaterials problems. This, alongside the high dimensionality and nonlinearity of available datasets in metal-based nanomaterials problems, makes it imperative to review recent advances in the implementation of ML techniques for these kinds of problems. In addition to understanding the applicability of different ML algorithms to various kinds of metal-based nanomaterials problems, it is hoped that this work will help facilitate understanding and promote interest in this emerging and less explored area of materials informatics. The scope of this review covers the introduction of metal-based nanomaterials, several techniques used in generating datasets for training ML models, feature engineering techniques used in nanomaterials-machine learning applications, and commonly applied ML algorithms. Then, we present the recent advances in ML applications to metal-based nanomaterials, with emphasis on the procedure and efficiency of algorithms used for such applications. In the concluding section, we identify the most common and efficient algorithms for distinctive property predictions. The common problems encountered in ML applications for metal-based nanoinformatics were mentioned. Finally, we propose suitable solutions and future outlooks for various challenges in metal-based nanoinformatics research.