We introduce a novel image registration framework, termed Zero-Shot Image Registration through Feature Extraction (ZSIR-FE), employing a pre-trained deep neural network for feature extraction. This framework is termed a zero-shot learning approach due to the non-overlapping nature of the training and testing datasets, coupled with the fact that the network modules within the overall network architecture are not trained for image registration. This approach eliminates the need for any training data specific to image registration, as it autonomously estimates the locations of significant features, which we herein termed as key points. Although the model provides the provision to fine-tune the key points as a hyperparameter, in our implementation, it remains fixed. This novel pipeline has been tested on the BraTS dataset, showcasing an enhancement in performance metrics, notably the Dice score, particularly for affine transformations. Moreover, this method yields instantaneous results for registration, irrespective of the input image size. The innovative framework of ZSIR-FE fosters a unified registration model, adept at addressing diverse medical imaging tasks and scenarios across varying domains.