The objectives of this study were to predict risk of metastatic breast cancer (MBC) recurrence dynamically from any point after 1 year of initial diagnosis in a BC patient’s journey. We show representative results for predicting 4 year risk post 1 year of date of diagnosis. We used a set of patients from the Concerto HealthAI database of oncology EMR data that includes clinical data from CancerLinQ Discovery to build this model. This data was further enriched by expert nurse curators. The study concluded that an AI model to predict risk of metastatic recurrence in breast cancer patients built using a real world dataset yielded promising results. Furthermore, analysis of input variables provided insights not only into the key features driving metastatic recurrence risk such as previous surgery, tumor subtype, stage & age at diagnosis etc. Such a model could be a useful for assessing patient risk & treatment options at various points in a breast cancer patients journey as well as stratify patients for different levels of surveillance.