Perception of human emotions plays an important role and is beneficial in today’s complex and diverse environment, where non-verbal communications are quite important. Facial expressions recognition is one of the tools to analyze human emotions. Healthcare, human-computer interface, market research, security, education, etc., are a few of the applications where facial expression recognition is prominently used. It contributes to improved communication, the development of empathic systems, the recognition of emotional states, and the facilitation of well-informed decision-making. We can predict behavior and enhance communication by correctly recognizing emotions from facial images. However, it can be difficult to accurately identify facial expressions due to the complex and multidimensional nature of human emotions. This study suggests that using transfer learning approaches can improve facial expression detection systems’ accuracy, resiliency, and versatility. Facial expression recognition, a novel technology for assessing emotions through facial cues, proves valuable across various fields. However, its complexity arises from the diversity of human emotions. This research proposes usage of transfer learning that enhances facial expression recognition models by improving their accuracy, reliability, and generalizability. This approach involves taking a pre-trained model over one dataset and adapting it to achieve superior performance over another dataset, thus mitigating the challenges of acquiring and training extensive facial expression datasets. It also reduces the risk of overfitting, making the models more effective across various real-world scenarios.