Algorithmic trading strategies have gained popularity in financial markets due to their ability to automate decision-making processes and capitalize on market trends swiftly. In this research, we propose a novel algorithmic trading strategy that combines machine learning techniques with the concept of Elliott Wave Theory for option trading (Cook in Cryptocurrency Trading J, 2018; Scott in J Commod Markets, 2019). The objective of this study is to develop a robust and predictive trading model that can identify potential trading opportunities in the complex and dynamic options market. The Elliott Wave Theory, a well-established technical analysis tool, provides valuable insights into market trends through wave patterns (Wilson in Forex Trading J, 2020; Thompson in J Tech Anal, 2021; White in J Precious Met Anal, 2021). To achieve our goal, we utilize historical market data and apply various machine learning algorithms to identify patterns and trends based on the principles of the Elliott Wave Theory. Data preprocessing and feature engineering are performed to ensure the accuracy and relevance of the input data. Machine learning model selection is a crucial step, where we evaluate different algorithms to identify the most suitable one for our trading strategy. The selected model undergoes rigorous training on the preprocessed data, learning from historical patterns and relationships. We then perform backtesting on historical data to simulate real-world trading scenarios and assess the performance of the proposed strategy. Key performance metrics, such as profitability and risk-adjusted returns, are utilized to measure the strategy’s effectiveness in generating profitable trades. Our research concludes with valuable insights into the potential benefits and limitations of the combined approach.