Age prediction is the task of extracting features from the human face image. Human aging factors can be expressed as multifactorial, gradual, time-dependent, physical, and biological damage. Attributes are extracted from a face image, and the aging factor depends on cells, tissues, and all living organisms. Human age prediction is distinct from chronological age prediction. Each human’s biological identity has unique characteristics. Age prediction depends on the maturity process of organs, other tissues, and cells. Many research works have been done on age classification using various techniques from human face images. It is a difficult task to the analysis of facial appearance. Issues in the existing algorithm are inefficient and require more computation time and storage space. To address these issues, this paper proposed a Deep convolutional neural network (DCNN) with a Cuckoo search algorithm (DCNN-CS). In this proposed work, DCNN-CS produces an effective age prediction from the human face image within a minimum execution time, handling a large dataset. The accuracy rate of the convolutional neural network (CNN) got 81.32, the Deep Neural Network (DNN) got 82.34, the Long short-term memory (LSTM) got 88.12, and the proposed work SLSTM-DNN got 91.45.

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