Abstract: Within the dynamic landscape of the financial markets, where fortunes can shift with the wind, the precision of future prediction holds immense value. This research embarks on a quest to enhance the accuracy of stock market forecasts, venturing into the domain of advanced time series models. We shall closely examine three prominent methodologies: the venerable ARIMA, the multifaceted VARMA, and the potent deep learning architecture known as LSTM. Through a rigorous investigation of their strengths and limitations, we shall subject these models to both univariate and multivariate data extracted from the Indian stock market. This comparative analysis aims to glean invaluable insights into their predictive capabilities, ultimately paving the path for the development of even more sophisticated forecasting tools. By wielding these instruments with confidence, investors can navigate the intricate dynamics of the market with a newfound certainty and optimize their decision-making for greater returns. Our endeavor transcends mere numerical analysis; it seeks to illuminate the complex dance of the market, unravel its hidden patterns, and ultimately empower investors to gain the upper hand in this challenging yet potentially rewarding financial sphere.