In recent years search engines have become the go-to methods for achieving many types of knowledge, spanning from detailed descriptions or general information interesting to the user. Likewise several reassignment techniques are capturing the attention of researchers in the field of signal analysis. Particularly, the Synchrosqueezing Wavelet Transform SST allows signal decomposition and instantaneous frequency extrusion, at the same time promising consistent reconstruction capabilities, hence the possibility to contrive an SST assisted inference engine. We are going to test it using datasets extracted from search engine trends, using a cloud of keywords related to the Bitcoin topic. This could be useful to study the evolution of the cryptocurrency both in time and geographical terms, and to estimate the future number of queries. The importance of Bitcoin queries prediction goes beyond the academic and research environments and, as such, it could lead to valuable commercial applications, such as financial recommender systems or blockchain-based transaction managers development.