This certification aims to provide a framework for visualising the developmental status of countries, use fuzzy logic as the basis with intermediate systems such as deep learning neural networks for a more detailed examination of the results. This system is created by collecting and analysing data from the USA, China, Russia, Japan, and Germany, continuously monitoring their advancement through various developmental phases. This study strategy involves using data sources via collection, revealing indices, and constructing a model that executes statistical calculations. This system employs data prediction techniques, including mathematical inference, linear regression, and deep learning neural networks, to assess and predict developmental patterns. Moreover, there is an opportunity to incorporate additional countries into subsequent analyses. Additionally, the research may be extended beyond the participating countries and customised to meet the user’s requirements. For the purposes of this research, the participants were selected according to their greater potential for advancement in numerous key areas. The choice of fuzzy logic was driven by its efficiency in making decisions marked by uncertainty and its importance in selection and planning strategies, being utilised in this system to manage the uncertainty and ambiguity of data in assessing the development level of the analysed countries. Subsequently, deep learning neural networks were employed for detailed data analysis and for making predictions regarding development trends, thereby providing a clear and comprehensive understanding of the complex processes involved. This system encapsulates various data processing capabilities within the Explainable Artificial Intelligence (XAI) terminology, using various data processing enhancement methods. The process of structuring information in the main system focuses on the use of conditional if–then rules, opting for formulations based on descriptive language instead of precise numerical values. This technique reflects the way people think, often using rules that are not precisely determined. The set of rules embedded in the core mechanism, which includes four types of input data and one category of output data, is designed not only to facilitate analytical evaluation according to system standards, whilst also upholding the principles of XAI. This allows the system not only to produce results, but also to provide a clear and transparent understanding of the processes and reasoning behind its decisions. The data of the values located by year were extracted for each country from the specialised documentation and were utilised in the statistics of the candidate countries, this constituting the input information in the analytical process. The study develops a perspective on progress in establishing supremacy for the countries examined and suggests several approaches to categorisation. Areas of trade growth, defence financing, science and technology innovation, and foreign investment are all covered. This study will examine the data obtained from system inputs and develop methods for predicting future values based on existing value ranges. The model under discussion is not exempt from constraints and dangers, which requires a rigorous and balanced analysis, since fluctuations in the estimates made can be affected by different parameters that can lead to varied results. The study is intended to be not only an analytical exercise, but also a robust contribution to understanding the complexity of state development in an ever-changing global landscape.