In this exciting work, we looked at how machine learning (ML) has been used in the field of use-wear analysis (that is, the study of the microscopic and macroscopic traces left on tools and artifacts from being used). We collected and reviewed nearly 50 case-studies spanning material types (lithics, bone, wood), ML algorithms, dataset sizes, and open science practices. We see that use-wear analysis is growing, but still quite limited in scope and diversity. Many studies use small datasets, often from the same institutions, focusing on just a few materials. Thus we argue that to make ML truly useful in this field, researchers need, greater dataset size and diversity, cross-institution collaboration, better documentation of methods, and open sharing of data and code. This review is a call-to-arms for archaeological scientists using ML, it shows what’s working, what’s not yet working, and what needs to change so that ML can genuinely help use-wear analysis rather than just being a flashy add-on. It helps researchers understand how to build better models, share better data, and ask better research questions.