Predictive analytics offers a powerful opportunity to improve women’s health after childbirth by preventing obstetric anal sphincter injuries (OASI). By integrating large-scale clinical data and patient-specific risk factors, predictive models can identify women at higher risk for OASI before or during delivery. Machine learning algorithms can analyze subtle patterns in maternal history, fetal characteristics, and labor dynamics that may not be apparent to clinicians, enabling proactive interventions such as modified delivery techniques or tailored postpartum care. This data-driven approach moves healthcare from reactive treatment to preventive precision medicine, enhancing recovery, reducing long-term complications like incontinence and pain, and ultimately improving quality of life for new mothers while optimizing healthcare resource utilization.