Machine learning models have played a significant role in Quantitative finance by helping in forecasting stock trends. This research paper explores the application of Long Short-Term Memory (GRU) forecasting models to develop option trading strategies for the NIFTY index, a leading benchmark in the Indian stock market. GRU, a type of recurrent neural network, is employed to forecast future movements in the NIFTY index. The study begins with an introduction to Options Trading and GRU forecasting. Historical NIFTY data is collected from (1990–2023) and pre-processed to train the GRU model, enabling it to make accurate predictions of future index movements. Based on the GRU forecast and the value of volatility obtained from the VIX of NIFTY, various positional option trading strategies are formulated incorporating the forecasted trend and expected volatility. Strategies include naked options, strangles, spreads, condors, and straddles. The strategies are back-tested on historical NIFTY data. The research aims to identify option trading strategies that demonstrate promising profitability and risk management capabilities when employed with GRU forecasts. The results and analysis offer insights into the potential benefits of utilizing GRU-based forecasting in options trading, with implications for investors seeking to optimize their portfolio strategies. Finally, the paper concludes with a discussion of the findings, limitations, and suggestions for further research. The proposed approach showcases the potential of combining advanced forecasting techniques with option trading strategies, opening new avenues for enhancing investment decision-making and risk management in the dynamic domain of financial markets.