Ethereum is one of the most established Blockchains. It has managed to hold on to second place in terms of market capitalization, after Bitcoin. Like all other cryptocurrencies, Ethereum is subject to normal price fluctuations and is equally affected by bear and bull markets. However, predicting the price of Ethereum remains a challenging task. This paper discusses the main drivers of Ethereum prices, including its attractiveness, macroeconomic and financial factors, with a particular focus on the use of Blockchain information in prediction models. We apply time series to daily data for the period from 30/07/2015 to 30/09/2023. We used Python and TensorFlow library version 2.11.0. Price prediction is performed with three machine learning techniques: support vector machines (SVMs), decision trees (DTs) and multilayer perceptron technique (MLP), for time-series analysis. The proposed approaches are used for price prediction in an industrial finance system and exhibit suitable accuracy scores. Two results are worth noting: When using the proposed model, the SVM algorithm provides better results than the MLP and the decision tree algorithms. The accuracy of the proposed model can be increased by adding features to the SVM algorithm. Also, Ethereum-specific Blockchain information is the most important variable in predicting Ethereum prices. This study highlights the importance of integrating Blockchain factors into cryptocurrency price prediction models. This conclusion improves investor decision-making and provides a reference for governments to design the best regulatory policies.