The enormous growth of blockchain in this cryptocurrency ecosystem has drawn attention to the ever increasing power consumption associated with blockchain aggregator systems. It is estimated that if the same trend continues, the energy consumption will be greater than some countries’ average annual power consumption. This exacerbates environmental issues such as pollution. In this chapter, we analyze Bitcoins and other cryptocurrencies for energy consumption from 2009 to 2024. We also attempt to understand the future energy consumption scenario too, the energy consumed for mining crypto, if we continue to mine Bitcoins (or any other crypto) in the same way. With the help of various Machine Learning (ML) methods like ARIMA, XGBoost, and Random Forest, we estimate the parameters like mining difficulty, mining hardware, and also the markets that influence the future energy demands. Out of all the ML models applied, Random Forest exemplified higher accuracy (lowest MAPE, MAE, RMSE). The outcomes signify Random Forest’s reliability while highlighting potential for future model improvements in cryptocurrency energy prediction. It is also observed that in order to mitigate energy usage for blockchain, usage of energy efficient consensus algorithms such as Proof-of-Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), Delegated Proof-of-Stake (DPoS), and usage of clean energy in Bitcoin mining are advocated. The machine learning capabilities of the blockchain package can further optimize the energy usage by predicting the demand and distributing the load on the mining site efficiently. The present advancements in technology and algorithms make it easier to predict the cost for the process of mining, avoiding wastage of energy.