1. A method of identifying and learning emotions in conversation utterances, the method comprising: receiving, by an integrated system, at least one of textual utterance data, audio utterance data and visual utterance data; fetching, by the integrated system, a set of facial expressions from the visual utterance data; annotating, by the integrated system, the set of facial expressions with corresponding set of emotions using predictive modeling; generating, by the integrated system, labelled data by tagging at least one of the textual utterance data and the audio utterance data with the set of emotions based on the set of facial expressions; providing, by the integrated system, the labelled data and non-labelled data to a self-learning model of the integrated system, wherein the non-labelled data comprises new textual utterance data received from a user, and wherein the self-learning model, learns, from the labelled data, about the set of emotions tagged with the textual utterance data, determines a new set of emotions corresponding to the new textual utterance data by using at least one of type of a recurrent neural network (RNN), generates new labelled data by tagging the new textual utterance data with the new set of emotions, and integrates the new labelled data into the self-learning model, thereby updating the self-learning model.