In this era of increased importance of mental health, the relevance of psychotherapy is nonpareil in enabling people to thrive with a better coping mechanism and emotional wellbeing. However, deaf and hard of hearing individuals are often left out from receiving proper mental health care due to the communication challenges in traditional talk therapy. Though researchers have been trying to understand the applications of sign language recognition (SLR) technologies and its effectiveness in order to enhance the life of deaf/hearing impaired community, no studies have addressed its importance in the clinical settings. Therefore, by focusing on the potential of Sign Language Recognition (SLR), we aim to explore the possible advancements towards embracing the diversity of language modalities. In the present review paper, research articles of past 5 years were reviewed to understand the possibilities and limitations of SLR technology. Findings shows that real time translation of sign language is possible using ML, which could enhance the quality of communication with the therapist. However, further research is necessary to understand other related factors such as dialect variations, facial expression integration and innovative clinically validated SLR systems. The current study incorporating both machine learning and SLR system foresee an inclusive future where deaf and hard of hearing individuals could also benefit from psychological care and mental wellbeing. Also, the study contributes to the growing body of literature on assistive technologies and transformative potential of ML in enhancing mental health care.