Age estimation is a fascinating field of study because its implementation can be utilized in a variety of contexts. Currently, the Convolutional Neural Network (CNN) technique is frequently utilized to solve the age estimation problem. However, this method has one disadvantage, namely a high computational cost that lengthens the training process. This paper proposes an alternative method for resolving age estimation issues. To overcome the shortcomings of the backpropagation method, the proposed method employs the Extreme Learning Machine (ELM) algorithm as a fully-connected CNN layer. ResNet50 and VGG16 are the CNN architectures employed in this study. Using the UTKFace dataset, the proposed method was trained and evaluated. Using ELM as a fully-connected layer on CNN provided significantly faster training time performance than the fully-connected layer trained with the backpropagation technique. In addition, the proposed combination of CNN and ELM can provide age classification performance that is competitive with state-of-the-art method for age estimation cases.