At the same time, recognizing the emotions, gender and age of a person is a vital issue in real life. This research focuses on identifying the emotions along with age and gender of different ages male, female, common gender and children. In this study, a modified convolutional neural network (M-CNN) has been proposed for feature extraction where classification is followed by a Multi Support Vector Machine (M-SVM). Instead of detecting the face, a head-detecting technique is also used in the prediction part. To detect the head we proposed a system using the You Only Look Once Version 3 (YOLOV3) network. It helps to extract the feature from the whole head instead of the only face. Three datasets named AR-2022, FER-2022 and GR-2022 are introduced in this study. We also include the common gender in our datasets. In the case of age classification, we categorized the age datasets into 11 categories depending on the similarity of their features, gender into 3 categories and emotions into 7 categories. Our proposed model is compared with other existing models and current research, where the proposed model gives better accuracy and the accuracy of classification of emotions, age and gender are 97.47%, 98.43% and 96.65% respectively.