Context. Machine learning (ML) is a key driver of technological innovation and increasingly relevant in everyday applications. Despite this, younger learners often lack accessible entry points into this complex field. Problem. While numerous educational tools and curricula exist for teaching ML, they typically focus on individual concepts like training or inference, often outside of a cohesive narrative. Few approaches provide a comprehensive, gamified learning experience that introduces core ML principles-such as classification, data quality, and model evaluation-within a single, story-driven environment tailored to younger learners. Objective. This thesis develops and evaluates an interactive ML challenge embedded in a narrative escape game designed to introduce students to ML concepts in an engaging and age-appropriate way. Method. The task was designed using educational theory and gamification principles and implemented in a Jupyter Notebook with pretrained ML models. Learners interact with classification tasks, train models, and make in-game decisions based on model predictions. Results. Asmall evaluation study indicated a modest improvement in self-assessed ML understanding and generally positive feedback regarding usability and visual design. However, motivation and emotional engagement varied among participants. Conclusion. The study suggests that gamified, interactive approaches can facilitate ML education for novices. Future work should refine the concept and expand evaluation to larger, more representative samples for reliable assessment of learning effects.