<p>Forecasting has a vital role in predicting future characteristics, helping manufacturing and industry. Technological forecasting is a mechanism for organizations to improve their technology approach. The precision of the forecasting is very essential for the high quality of the outcomes and hence, for the future of the company. However, despite the availability of several predictive mechanisms, there is a lot of uncertainty and ambiguity when choosing the most effective method among the different methods. It is very important for decision-makers to make reliable and rational decisions in a timely manner because wrong decisions can cause serious financial losses as well as social instability. In order to identify a solution to this issue, this paper developed an extended CODAS method based on the innovative concept of (p, q)-fractional fuzzy numbers in which the decision makers and the weight of criteria are completely unknown. Moreover, a novel series of (p, q)-FF Yager aggregation data is specified together with an in-depth description of the key features of the created aggregation operations. Using the anticipated series of (p, q)-FF Yager aggregation operations and the extended CODAS approach to obtain the most optimal alternative. In the end, a genuine decision-making challenge is implemented to choose the appropriate technological forecasting method, which demonstrates the accuracy and applicability of the projected methodology. The outcomes proved that the patent analysis method was determined to be the best suitable technique for technological forecasting.</p>