The US Dollar Index is an important indicator of the global economy, as it measures the value of the US dollar against a basket of other currencies. The Dollar Index is used by investors, traders, and decision-makers to inform their investment and trading decisions, as well as to monitor the health of the global economy. In recent years, machine learning techniques have gained popularity in the field of finance for their ability to analyse large amounts of data and provide accurate predictions. This study explores the use of machine learning techniques for predicting the Dollar Index. The study compares the performance of different machine learning algorithms, including Random Forest, Support Vector Machines, and Artificial Neural Networks, in predicting the Dollar Index. The study uses daily data on the Dollar Index from January 2000 to December 2020, which is pre-processed and normalized before being used in the machine learning models. The study finds that machine learning models outperform traditional methods in predicting the Dollar Index. The Random Forest algorithm performs the best among the models tested, with an accuracy of 98.5%. The study also provides a detailed analysis of the feature importance of the input variables in the prediction models, which can help decision-makers understand the factors that affect the Dollar Index. The study concludes that machine learning techniques can provide decision-makers with valuable insights for their investment and trading decisions. The study suggests that future research can explore the use of other machine learning algorithms and input variables to improve the accuracy of the prediction models. The study also highlights the importance of using machine learning techniques in finance and economics, as they can help investors create strong portfolios with little risk.