1. An integrated system for identifying and learning emotions in conversation utterances, the system comprising: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to: receive textual utterance data, audio utterance data and visual utterance data; fetch a set of facial expressions from the visual utterance data; annotate the set of facial expressions with corresponding set of emotions using predictive modeling; generate 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; provide 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; and convert the audio utterance data into corresponding textual utterance data by using speech-to-text translation.