Stress analysis is crucial for understanding mental health and improving well-being. Electroencephalography (EEG) has recognised as a prominent tool for non-invasive stress detection due to its ability to capture brain signal. It records electrical activity of brain essential in early detection of stress. The research has suggested a number of Deep Learning (DL) techniques such as CNN, RNN, DNN, LSTM etc. for evaluating mental stress though there are various neuroimaging methods have been used to evaluate stress of human being. This review paper examines the recent advancements in stress analysis using EEG, focusing particularly on studies from the last five years that employ deep learning techniques. The review draws attention to the significant discrepancies among the research findings and makes the case that different data processing techniques lead to a number of contradicting conclusions. A number of factors, such as absence of a consistent protocol, the type of stressor, the brain area of interest, the duration of the experiment, appropriate EEG data analysis and the feature identification, extraction mechanism, and the type of classifier, could be responsible for the variances in the results. The incorporation of deep learning with EEG has shown significant potential in enhancing the precision accuracy and efficiency of stress identification systems.