In the dynamic and challenging maritime domain, Search and Rescue (SAR) operations are critical for ensuring the safety of life at sea. Adverse weather conditions often hinder traditional SAR efforts, leading to significant delays or cancellation of search missions. This paper introduces an autonomous search system utilizing Unmanned Aerial Vehicles. The system combines decision-making techniques for automatic mission generation and a flexible machine-learning framework that allows for easy training and deployment of models to automatically process data gathered during SAR operations. One of the system’s main features is the ease of use in mission planning, where high-level mission goals can be specified via a user interface in the form of data requests. The paper presents the results of the experimental evaluations of the system and showcases its deployment in actual field-test experimentation.