Age detection can be a challenging problem due to the variations in the aging of every individual depending on one’s health, lifestyle, etc. Analyzing the face of humans using computer vision can help in estimating the age of humans as the face holds most of the important attributes. However, the corona virus disease 2019 (COVID-19) pandemic has forced people all over the world to wear face masks to prevent human-to-human transmission of the virus thereby making it difficult to detect the age of the person wearing a mask. The analysis of a human face without a facial mask in the images provided by cameras or webcams is a tedious process as the human face in an image can have variations due to changes in position, orientation, and a lot of other factors such as lighting conditions, image resolution, etc. Thus, analyzing a human face with a facial mask becomes even more difficult as most of the prominent facial attributes such as the nose, wrinkles on cheeks, etc. are not visible due to the face mask. To overcome this problem, we have proposed a system to perform age detection using FaceMaskNet-9, a deep learning network that will detect the age of the person with a face mask. We have used a deep learning-based age detector model for age prediction. The FaceMaskNet-9 used for the process of predicting the age of a person increases the accuracy of the task and precisely classifies people with masked faces into different age groups. Our age detector can be used in CCTV footage, for medical diagnosis, for recommending videos and advertisements according to the target audience, to give extra privileges to people depending on their age and even on social media where there are age restrictions to view or use certain contents on the social platforms.