The usage of social media platforms has increased tremendously in the past decade. Twitter is one of the most popular platforms for sharing opinions and feelings on various topics, organizations rely on Twitter data to analyze and gather insights for their businesses using Natural Language Processing (NLP) techniques. However, there are various challenges in analyzing such a huge volume of tweets that are in text format. One such challenge is to identify irony in tweets, which has a significant impact on analyzing sentiments. To overcome this challenge, we propose a solution that automatically recognizes the presence of irony in text and classifies the type of irony. The solution consists of two tasks: Task A performs binary classification to annotate whether irony is expressed or not in each tweet, while Task B performs multi-class classification to classify the type of irony expressed in the tweets. We used the dataset from the “SemEval-2018 Task 3: Irony detection in English tweets” challenge to train and test the proposed solution. The dataset contains 3,817 English tweets for training and 784 English tweets for testing both Tasks A and B. We developed different machine learning models by leveraging traditional classifiers, neural networks, and large language models. Our UNF-IDT (Irony Detector in Text) model developed using BERT (Bidirectional Encoder Representations from Transformers) was able to achieve an F1 score of 0.757 for Task A (Binary Classification) and an F1 score of 0.449 for Task B (Multi-class Classification), retrospectively obtaining 4th and 9th ranks, in the two tasks respectively.