Graphology, the study of handwriting analysis, has for quite a while become a topic both of interest and skepticism in the scientific world. Although the preconceived graphology methods have been lambasted for their non-objective nature and lack of termed, that is, theoretical testing and plausibility and verification through testing data, the opportunity that handwriting may tender to individuals on their personality as well as their emotions still is an exciting area of the study. This paper introduces a step that strengthens the link between the classical graphology and the artificial intelligence technologies by suggesting a new methodology for handwriting analysis that employs deep learning networks in a collective fashion. This paper provides a new approach to the analysis of handwriting, in which manually derived principles and algorithms in artificial intelligence are work out for graphology. This paper presents a concept of how to identify the emotions and personality traits from handwriting using a deep learning architecture that is multimodal. Our approach integrates CNNs that are used for image processing, RNNs that are used for sequence analysis, and transformer models that are used for contextual understanding. The ensemble method performs significantly better than both traditional graphology techniques and non-ground techniques, obtaining 85% accuracy in sentiment classification and 78% in character trait identification in handwriting samples of diverse personalities.