In recent years, stock trading has emerged as a pivotal aspect of the corporate landscape, garnering increasing significance. The corporate realm invests substantial time in both the execution of stock trades and the anticipation of stock prices. Extensive research endeavours have been undertaken to enhance the precision of stock price predictions. Traders leverage this information to capitalize on favourable price movements, while investors strategically allocate their resources based on projections of which stocks are poised to augment their net worth. Diverse methodologies are employed in the pursuit of stock price prediction. This study delves into the application of the ARIMA (Autoregressive Integrated Moving Average) model for forecasting stock prices. The paper includes pertinent R command lines and outputs for elucidation. The construction of the model relies on historical data sourced from Yahoo! Finance, specifically from the NASDAQ stock exchange. Estimations are executed using the forecast and predict packages in R. The outcomes are then juxtaposed with real-time stock prices, revealing promising results. The findings underscore the robust potential of ARIMA models in achieving accurate stock price predictions.