Autologous breast reconstruction surgery is a vital part of the recovery process for patients with breast cancer. While various reconstructive options exist, the deep inferior epigastric artery perforator (DIEP) flap is often favoured for its ability to closely mimic natural breast tissue. However, the complex vascular anatomy associated with the deep inferior epigastric artery (DIEA) presents challenges for surgeons during DIEP flap execution. Preoperative imaging, such as computed tomography angiography (CTA), is commonly used to understand vascular architecture and aid in selecting appropriate perforators. Conventional reporting of CTA scans is a labour-intensive process that can be challenging and requires specific expertise. The integration of artificial intelligence (AI) and machine learning (ML) algorithms in medical imaging has the potential to address these challenges. AI can enhance CTA through improved data acquisition, image post-processing, and potentially interpretation. By automating the perforator selection process, AI applications can significantly reduce the time spent on preoperative imaging analysis and potentially improve accuracy and reliability. While AI shows promise in optimizing efficiency, accuracy, and reliability in breast reconstruction planning, challenges and ethical considerations need to be addressed. This article explores the challenges, opportunities, and future directions of using AI in the preoperative planning of autologous breast reconstruction.