People who have learning disabilities frequently struggle in the areas of reading and writing. The main effects of learning disabilities are bad grades and a lack of motivation that lasts a lifetime. Most of the time, it starts in childhood and has a big effect on academics. The early recognition by using technological prediction will assist the academicians to devise special learning strategy to accommodate such children in the teaching learning and hence perform well in academics. Specific Learning disability is a complex neurodevelopmental/neurological disorder that affects many children. Learning Disability have two major categories like Specific Learning Disability in reading (dyslexia) and Specific Learning Disability in writing (dysgraphia). Our primary focus is to predict the symptoms of dysgraphia using deep learning algorithms. Deep learning algorithms are general-purpose methods of artificial intelligence that can learn patterns from data without the need to define them apriori. Convolutional Neural Network is a type of deep learning algorithm that is particularly well-suited for image recognition and processing tasks. To generate automated and trained models that reduce the amount of human intervention by significant margins is the primary goal of deep learning. For experimentation purpose, we have used MNIST alphabets dataset to analyse the problem. For this research, we have reviewed several methods like handwriting analysis, eye tracking, Tests, questionnaire, games; etc but we choose handwriting modality for our research. In this paper, experimental study done on convolutional neural network and transfer learning models for predict the dysgraphia symptoms and implemented using publicly available dataset of MNIST alphabet dataset. We compared the accuracy of the VGG16, VGG19, and ResNet50 architectures. Based on the analysis, we have arrived at the conclusion that the VGG16 architecture is the most superior option in training and validation phase.