Hybrid animal classification presents a unique challenge due to the complex genetic makeup resulting from interbreeding between different species. In this research, a novel approach for hybrid animal classification utilizing machine learning techniques is proposed. Leveraging a custom-made dataset encompassing a diverse range of hybrid species, and introducing a transfer learning methodology to address the inherent data scarcity and domain shift issues. The proposed method effectively extracts and transfers knowledge learned from pre-trained models, adapting them to the specific characteristics of hybrid animal classification. Through extensive experimentation and evaluation, the efficacy of the proposed approach is demonstrated in accurately classifying hybrid animals, outperforming traditional classification methods. This research contributes to advancing the understanding and application of machine learning in the domain of hybrid animal classification, with potential implications for biodiversity conservation and genetic research.