We apply machine learning methods to uncover nuanced trends in Canadian church dynamics, employing time-series clustering to group congregations with similar growth or decline trajectories. Through change point detection, we pinpoint significant shifts in church participation and giving patterns, while our classification models identify distinct profiles of church health and community engagement, enabling precise strategic responses.