<p>Facial expression recognition (FER) is an important area in computer vision and Artificial Intelligence focused on identifying emotions from facial movements. However, FER faces challenges such as variability in expressions, occlusions, and changing lighting conditions. Addressing these requires advanced solutions, including improved feature extraction techniques, enhanced model training processes, and comprehensive contextual data analysis. This paper proposes a multi-aspect enhancement learning based facial emotion recognition (MAEL-FER) model that exploits several unique modules to effectively classify facial emotions. This approach consists of one of the most specific features to learn the best details of facial expressions that allow for the classification of similar emotions. A feature enhancement module enhances features in some way and suppress those that may not be accurate or noisy, a region enhancing module enhances regions in face such as the eyes and the mouth to recognize emotions. Also, the model entails feature such as generalization learning for better adaptation with various dataset, Meta-learning to allow efficient learning with limited samples, Adversarial training for robustness against noise and adversarial attacks. The MAEL-FER model offers high accuracy in recognizing facial expressions and effectively extracts features, enabling it to distinguish between similar emotions even in challenging conditions. Simulation results highlight the MAEL-FER model's effectiveness with high accuracy rates of 85.78%, 96.98%, 94.83%, and 69.08% across four FER datasets of FER-2013, CK+, RAF-DB, and AFFECTNET-7, respectively. These results surpass previous state-of-the-art methods, demonstrating the model's superior performance, reliability, and efficiency in diverse FER tasks and conditions.</p>