In biomedical data analysis, the advancement of deep learning techniques faces challenges stemming from the scarcity of ethically sourced datasets and the costs associated with data acquisition. In response to these obstacles, We propose HybridMorph, a novel hybrid model for biomedical image registration, which combines the strengths of synthetic and real data to achieve competitive error rates with state-of-the-art models. It is a pairwise medical image registration model built upon the foundations of VoxelMorph [1] and SynthMorph [2]. Our research showcases the efficacy of transferring knowledge gleaned from Synthmorph models to fine-tune weights for specific tasks, culminating in successful trials of few-shot learning and rapid adaptation capabilities. These methodologies illustrate how hybrid approaches can streamline training processes, mitigate computational resource demands, and alleviate the substantial data prerequisites inherent in contemporary deep learning models. Furthermore, our exploration into transfer learning underscores the potency of such techniques, paving the way for examining meta-learning, multi-task learning, and other avenues to mitigate data dependencies and complexity. Moreover, we are pleased to offer open access to our code repository at: https://github.com/CaffineAddic/HybridMorph .