Using 10-fold cross-validation, the proposed system achieved an overall accuracy of 99.19%, representing a 6.45-point improvement. Furthermore, the LSTM+DNN classifier demonstrated improvements in MER accuracy of 1.61, 1.61, and 3.23 percentage points compared to k-nearest neighbor (k-NN), support vector machine (SVM), and random forest classifiers, respectively. In [4], the paper emphasizes the importance of mental health, which is often neglected despite its crucial role alongside physical health. Recognizing that emotional well-being can significantly impact overall health, including increasing the risk of conditions like depression and diabetes, the study proposes a system for emotion detection and mood enhancement through music recommendation. Leveraging deep-learning and image-processing techniques, the system employs a CNN based on the LeNet architecture for emotion detection. The KDEF dataset serves as the input for training the CNN model to recognize seven distinct emotions, namely sad, disgust, happy, afraid, neutral, angry, and surprised, based on facial expressions. The trained model achieved a training accuracy of 98.03% and a validation accuracy of 97.96% in correctly classifying these emotions. The increasing volume of music necessitates improved organization, classification, and retrieval methods, particularly given the growing user demand for personalized experiences that consider both the creator's emotional expression and individual psychological characteristics [5]. Current MER methods face challenges in accurately capturing the dynamic emotional shifts within a piece of music and effectively analyzing the nuanced emotional content conveyed beyond essential musical elements like pitch, length, and intensity. To address these limitations, this paper proposes an improved back propagation (BP) neural network for music data analysis. Recognizing the tendency of traditional BP networks to converge on local optima, the artificial bee colony (ABC) algorithm is integrated to enhance the network's structure. Specifically, the ABC algorithm's output provides the initial weights and thresholds for the BP neural network, enabling the ABC algorithm to adjust these parameters dynamically and subsequently feed the optimized values back into the BP network. This integration aims to improve the global search capability of the BP network, mitigate the risk of converging on local optima, and accelerate convergence speed. The experimental results obtained from testing on public music datasets demonstrate that the proposed ABC-optimized BP network achieves superior recognition performance and faster processing speeds than other benchmark models. Existing music recommendation systems often rely on content-based, collaborative, hybrid, or context-based approaches, which are frequently limited by the size of their song databases [6]. The study analyzes these existing systems and proposes a novel content-based recommendation system to accommodate a significantly more extensive music library. The system incorporates user emotion as a key input, recommending songs based on the user's current emotional state and prior listening history. Song features are retrieved using Spotify's Web API. The emotion recognition model employed achieves an accuracy of approximately 67%. The resulting recommendation system successfully generates a 20-song playlist tailored to the user's Spotify listening history and the detected current emotion. Music is vital in human life, serving as a powerful medium for emotional expression and evocation. The diverse nature of music leads to varied listener experiences, with different musical pieces inducing a range of emotions and even the same piece eliciting distinct feelings based on the listener's psychological state [7]. Consequently, MER has garnered significant attention in both the academic and industrial spheres. Leveraging advancements in brain science, MER has found applications in diverse fields, including recommendation systems, automated music composition, psychotherapy, and music visualization.