This study investigates the potential of information field monitoring for the early diagnosis of global catastrophes, addressing the need for timely information in mitigating catastrophic treats. The article aims to develop a novel approach for identifying potential global crises through information field analysis. Based on a literature review, the study addresses the limitations of existing forecasting methods, which often fail to capture the complexity and unpredictability of sudden or rapidly evolving events. The proposed framework incorporates a four-stage data refinement process, transforming unstructured data into actionable insights. The main outcome of the study is a detailed methodological scheme that describes the sequential transformation of the information field into data, information, knowledge and wisdom about emerging catastrophic processes. The methodology integrates techniques such as Natural Language Processing (NLP), machine linguistics, machine learning, and deep learning, focusing on the sequential conversion of data into information, knowledge, and wisdom. The data for this process can be obtained primarily from social media and other digital platforms, where early signals of emerging crises can be detected and analyzed. Findings suggest that monitoring the information field can provide a reliable basis for early diagnosis by identifying weak signals in digital communication trends and public sentiment. The study’s results show that the proposed model can effectively identify precursors to potential crises, offering a proactive approach for improving risk management and enhancing economic security.