This study investigates the time-varying efficiency of major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC) and XRP—within the framework of the adaptive market hypothesis (AMH). We introduce a forward-looking method by integrating predicted data into the Dominguez–Lobato (DL) and generalised spectral (GS) testing frameworks as part of Martingale Difference Theory (MDT). Our strategy allows us to forecast potential future inefficiencies in the market, advancing the traditional retrospective analyses (based on historical perspective) prevalent in the literature. We employ and test forecasted data to identify potential future shifts in market efficiency, as an extension of the Martingale difference hypothesis (MDH). The results indicate that cryptocurrency markets do not maintain a static level of efficiency but adapt over time, with varying degrees of predictability and inefficiency. The random forest (RF) model demonstrates the ability to forecast breaks in market efficiency.