Deep learning is a subset of machine learning that uses multi-layered neural networks called deep neural networks, to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of the artificial intelligence (AI) in our lives today. Facial emotion-based music recommendation is a promising area of research in the field of music information retrieval. The ability to recognize facial expressions of emotions can be used to personalize music recommendations and improve the user experience. In this study, we propose a facial emotion-based music recommendation system using convolutional neural networks (CNNs). The proposed approach involves using a CNN model to extract facial features from images of users' faces. The CNN is trained on a large dataset of facial images labelled with emotional categories. The extracted features are then used to train a music recommendation model that can recommend music based on the user's facial expression of emotion. We evaluate our approach on a publicly available dataset and show that it outperforms existing state-of-the-art methods. Our results demonstrate the effectiveness of using facial emotion recognition and CNNs for personalized music recommendation. The proposed facial emotion-based music recommendation system using CNNs involves several steps. First, a dataset of facial images labelled with emotional categories is collected. This dataset is used to train a CNN model that can extract high-level features from facial images that are representative of different emotional states. Next, the extracted features are used to train a music recommendation model that can recommend music based on the user's current facial expression of emotion, Overall, the proposed approach has the potential to enhance user experiences by providing personalized music recommendations based on their current facial expression of emotion. It also contributes to the development of more effective music recommendation systems that take into account users' emotional states.