Abstract Developing an automatic age estimation and majority age classification towards human faces continue to possess an important role in computer vision and pattern recognition. In the experiment, three well-known benchmark datasets, MORPH-II, FG- NET, and CLAP2016, are adopted to validate the procedure. We are using the new method, estimation of age using the MRI data. The data from the MRI image is extracted by the use of feature extraction in the machine learning process. Using the MRI data we can easily analyze the age of a human. Here we are going to implement the machine learning algorithm to identify the age and the estimation of age of a person by using this method we can get high accuracy than the traditional method. We have used Neural Network for classification and Discrete wavelet transform by using methodologies DWT, GLCM Feature Extraction and NN Training and Classification. The experimental results show that the performance can be significantly improved by using our framework and this framework also outperforms several state of the art age estimation methods. Our model can use for predicting the age from the image accurately and also used for classifying the age segregation. It is used in Forensic applications, government documents cross checking applications, passport applications inairport.