Machine learning (ML) is transforming atmospheric chemistry, offering powerful tools to address challenges in tropospheric ozone research, a critical area for climate resilience and public health. As in adjacent fields, ML approaches complement existing research by learning patterns from ever-increasing volumes of atmospheric and environmental data relevant to ozone. We highlight the rapid progress made in the field since Phase 1 of the Tropospheric Ozone Assessment Report, focussing particularly on the most active areas of research, namely short-term ozone forecasting, emulation of atmospheric chemistry and the use of remote sensing for ozone estimation. Despite these advances, many challenges in the field remain, including the quality of data, benchmarks, and limited model generalisation and explainability. This review provides a comprehensive synthesis of recent advancements, highlights critical challenges, and proposes actionable pathways to further advance ML applications in ozone research. Achieving this potential will require close collaborations across atmospheric chemistry, ML and computational science, aimed at addressing key challenges such as the development of global benchmark datasets and robust, explainable models.