The article is devoted to revealing the role of cognitive data visualization technology in the development of interpretive competence of engineering students in modern conditions when teaching creativity. The relevance of the work is conditioned by the increasing requirements to the ability to analyze, critically reflect and reliably interpret multidimensional information, as well as to generate non-standard solutions and find new meanings in the conditions of digitalization of education. The aim of the research is theoretical substantiation and practical evaluation of the effectiveness of cognitive data visualization technology as a tool for the development of interpretive competence in future engineers during creativity training. The pilot study confirmed the high efficiency of cognitive data visualization technology using artificial intelligence tools for developing interpretive competence and creativity in engineering students. The use of cognitive data visualization technology led to a statistically significant increase in the level of interpretive competence in the experimental group: the average scores for key components increased significantly, for example, information processing, creativity of interpretation. Students’ readiness to work with visualized data increased by 27%, which was confirmed statistically (p ≤ 0.05), and the time for visualizing solutions decreased by 15–20%, confirming a decrease in cognitive load. An analysis of 42 defended case studies showed high quality of work: 39.62% were completed at a high level (84–100 points), 47% at an average level (77–83 points), while an evolution of work was observed from abstract concepts to specific products. Students responded positively to cognitive data visualization technologies, noting that visualization makes learning “tangible” and that AI tools act as “co-authors” that accelerate activity and enhance motivation for creative activity. Significant risks were identified: 21% of respondents demonstrated a tendency toward “data aestheticization” to the detriment of the depth of analysis, and 14% encountered technical difficulties when working with AI. The conceptual risk of narrowing and dehumanizing the understanding of reality with excessive dependence on the schemes defined by computing systems was identified. The practical significance of the work lies in the development of a methodology that can be integrated into various disciplines and specific recommendations for teachers: combine cognitive data visualization technologies with reflective practices; use AI as a support (not a substitute) for creative thinking; adapt tools to disciplinary specifics; develop a critical attitude towards AI content to prevent template solutions. Cognitive data visualization technologies have proven their role as a “cognitive amplifier” for training future engineers who are able to analyze multidimensional information and generate non-standard solutions under conditions of uncertainty.