The MARTA-AF model analyzed 437 atrial fibrillation patients who underwent catheter ablation, using pre-procedural bi-atrial imaging, clinical data, and procedural characteristics to predict not just whether, but when AF would recur over three years. The system successfully stratified patients into distinct low- and high-risk groups with sustained statistical discrimination throughout follow-up, notably finding that right atrial shape features enhanced prediction accuracy. This represents a meaningful advance in personalized cardiac care, potentially transforming post-ablation management from reactive monitoring to proactive intervention timing. Current AF management relies on population-level protocols because clinicians lack tools to predict individual recurrence timelines. MARTA-AF's time-varying risk forecasts could enable precision medicine approaches—optimizing medication timing, scheduling targeted monitoring, and counseling patients on lifestyle modifications based on their personal risk trajectory. However, this retrospective analysis of 437 patients requires validation in larger, prospective cohorts across diverse populations. As a preprint awaiting peer review, these promising results need independent verification before clinical implementation. The multimodal AI approach combining imaging with clinical data represents an incremental but practically significant step toward individualized cardiac rhythm management.