Reliable waiting time predictions are essential for customer satisfaction during roadside assistance, yet difficult to achieve in dynamic operations. In this session, we show how we partnered with ÖAMTC to develop machine learning models that significantly improved prediction accuracy over the existing approach. We share practical lessons on choosing the right models, addressing data limitations, and continuously improving predictions through operational feedback and new data.