Farmers in Zambia, Zimbabwe and Malawi face significant challenges in cattle management due to livestock theft, unreliable traditional identification methods and poor records keeping. This paper presents a biometric and AI-driven cattle identification system specifically designed for farmers in Malawi, Zambia, and Zimbabwe. The proposed system leverages convolutional neural networks (CNNs) for facial recognition of individual cattle, integrated with a mobile application for field deployment. Our methodology involves collecting high-resolution facial images from 1500 cattle across diverse breeds and environmental conditions in the three counties, developing a CNN-based model using transfer learning with ResNet-50 architecture, and creating a Flutter-based mobile application with offline capabilities and cloud synchronization. Preliminary results demonstrate 88% accuracy in field tests. The system successfully distinguishes between known and unknown cattle in 85% of open-set scenarios, significantly enhancing herd security. Usability studies with 30 users across the three regions show 75% rating the application as “easy to use,” with particular appreciation for offline functionality. The proposed solution addresses critical gaps in livestock traceability, reduces theft incidents, and improves record management, ultimately enhancing economic stability for smallholder farmers. This research contributes a scalable, user-centric approach to digital livestock management, with potential for broader application across sub-Saharan Africa.