Selecting out the best embryos was a key part of the IVF process, which we did by implanting only high-quality ones and not the poor ones, which may cause failure of the pregnancy or, in some cases, a miscarriage. We developed a machine learning system that looks at images of the embryos via a microscope and reports which are good and which are not. Our approach first did background noise removal to improve image quality, then we did contrast enhancement, which in turn improved the detail put forward. Also, we used data augmentation to increase the diversity of our samples and also to prevent overfitting during the training phase. We looked at the whole structure of the embryo as well as fine surface details which may point out development issues; we used a mix of traditional hand-designed features and deep learning-based embeddings for better results. A hybrid ML model was trained using these features and, in the real world, was able to perform with a prediction accuracy of 96.94% attained during testing. The occlusion-based visualization method, which served to bolster confidence and transparency, indicated to the user which regions of the embryo image were relevant to the prediction. This approach, which was objective and non-invasive, assisted embryologists in confident decision-making to decrease implantation failure and improve success rates of IVF treatments globally. This approach, which was objective and non-invasive, assisted embryologists in reducing the risk of implantation failure and ultimately increasing the success rates of IVF treatments worldwide.