Hyperspectral imaging (HSI) has emerged as a powerful tool for environmental monitoring, particularly in the detection and analysis of hydrocarbon spills. This study presents an IoT-based framework for the collection, management, and analysis of hyperspectral data in a controlled experimental setup simulating hydrocarbon contamination. Using a state-of-the-art hyperspectral camera, we generated a comprehensive dataset that integrates contaminant behavior over time. The data was systematically captured, processed, and stored in a cloud-based repository following IoT principles, ensuring accessibility and scalability. This work addresses the current scarcity of specialized hyperspectral datasets for hydrocarbon spill analysis and sets the foundation for developing robust predictive models using machine learning techniques. Additionally, ethical and sustainable database design practices were incorporated to enhance the dataset’s usability for environmental and scientific applications.