In the present era of digitalization, sentiment analysis plays a significant role for understanding public opinion, customer feedback, and social trends on different media channels. The sentiments are equally important for both businesses and individuals as these are now expressed through text as well as emoticons and images. With the vast growth of textual and visual data alongside emotions on the web, a need for an all-round sentiment analysis model has risen sharply. However most of the existing methodologies turn out to be myopic; they lack the ability to cohesively analyze sentiment from all three sources (text, emoticons, images). Our model seeks to address this limitation by adopting various machine learning techniques that enable seamless processing and interpretation of sentiment from diverse data repositories. We are proposing a comprehensive sentiment analysis tool that combines advanced techniques to perform sarcasm detection, rule-based and machine learning models for text sentiment analysis, emotion mapping for emoji-based sentiment analysis, and for image-based sentiment analysis. By integrating advanced machine learning techniques we look forward not just for providing but also packaging sophisticated details about public sentiment which will be visually delivered (like graphical reports) so that decision makers can better understand and act upon them.