This study introduces "Ice and Fire," a Multi-Task Learning (MTL) dataset tailored for sentiment analysis in the Icelandic language. It encompasses a wide range of linguistic tasks, including sentiment and emotion detection, as well as the identification of toxicity, hate speech, encouragement, sympathy, sarcasm/irony, and trolling. With 261 fully annotated blog comments and 1,045 comments annotated in at least one task, this contribution marks a significant step forward in the field of Icelandic natural language processing. The dataset provides a comprehensive resource for understanding the nuances of online communication in Icelandic and an interface to expand the annotation effort. Despite the challenges inherent in subjective interpretation of text, our findings highlight the positive potential of this dataset to improve text analysis techniques and encourage more inclusive online discourse in Icelandic communities. With promising baseline performances, "Ice and Fire" sets the stage for future research to enhance automated text analysis and develop sophisticated language technologies, contributing to healthier online environments and advancing Icelandic language resources.