<p>This research aims to use curve estimation methods to analyze and establish the relationship between the stock price changes of the four Indian IT companies, TCS, Infosys, HCL, and LTIMindtree, between the period of September 4, 2023, and September 2, 2024. Diverse forms of regression models are employed in the study, including Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, and Exponential to compare their ability to represent stock price trends. The comparison is conducted using goodness-of-fit measures such as R-squared, which indicates the explanatory strength of the models. The results indicate that the cubic regression model explains a comparatively higher proportion of stock price variation across the four companies based on in-sample goodness-of-fit measures. The degree of price volatility of TCS, Infosys, and HCL differs, but the average stock price and volatility are the highest in the case of LTIMindtree. These findings provide useful insights for stakeholders and investors, and emphasize the importance of selecting appropriate curve-estimation techniques for analyzing stock price fluctuations in the Indian IT sector. This research is also an addition to the current literature since it is the first sector-specific comparative analysis of the established curve-estimation models of major Indian IT stocks. The study emphasizes the suitability of different traditional regression forms for representing the distinctive volatility characteristics of the IT industry, rather than proposing new prediction algorithms. The results offer practical implications for investors and analysts by demonstrating that non-linear models, particularly the cubic form, provide clearer trend interpretation and support short-term strategic decision-making.</p>