Due to the advancement of the methodologies employed in this field and the increased attention being paid to the deep learning (DL) techniques’ implementation, focusing on convolutional neural networks (CNNs), gender and age estimates have recently assumed a significant amount of relevance. It is important to precisely predict the gender, including the age of a person, provided that it is used in many applications for smart devices, including those related to security, health, and other areas. Although there have been several studies and research in this area, gender, and age estimation still confront certain problems and difficulties, such as existing of earrings, races, masked faces, makeup, etc. which might interfere with the systems’ operations and decrease their accuracy. In this paper, we assess the accuracy of the models employed in three of the most well-known datasets: MORPH2, FG-NET, and OUI-Adience. Our focus is on the best and most recent technology available in this field. Additionally, we have mentioned a list of most of the challenges that may face in the process of estimating age and gender, as well as a list of applications and areas in which it can be used.