In today's troubled economies, nations are competing in many aspects, including innovation and knowledge progress. Even though there are many composite indicators to measure knowledge and innovation at both micro and macro levels, benefits to decision makers still limited due to numerous progress indicators, without any unified, easy to visualize and evaluate forecasting capabilities. This paper introduces a novel approach to forecasting and finding the aggregated position of many Knowledge-Based Economy (KBE) with a high degree of accuracy. The suggested approach is based on data mining, Neural Networks, Principle Component Analysis (PCA), and Self-Organising Map (SOM). The proposed model has the capability of forecasting and aggregating five major KBE indicators into a unified meaningful map that places any KBE in its league regardless of incomplete missing or little data. The Unified Knowledge Economy Forecast Map (UKFM) reflects the overall position of homogeneous knowledge economies, and it can be used to visualise, identify or evaluate stable, progressing or accelerating KBEs.