Abstract:    This study aims to predict stock price movements of Tata Consultancy Services (TCS), a global IT services leader, using time series and machine learning techniques. Accurate stock price prediction is essential for investors and traders to make informed decisions. This paper discusses about  various machine learning models, including linear regression, decision trees, and neural networks, to forecast TCS’s stock prices based on historical data and technical indicators. The dataset, sourced from Yahoo Finance, includes monthly stock prices, as well as open, close, high, low prices, and trading volume from January 2014 to December 2024.Our findings evaluate the strengths and limitations of each model in forecasting stock prices. The results suggest that machine learning models can significantly enhance prediction accuracy, offering valuable insights for investors. This study contributes to the growing field of financial forecasting and has potential applications in automated trading systems. Experimental results show that machine learning models outperform traditional models in terms of prediction accuracy across all metrics.