This study offers a comprehensive way of analyzing the financial market and giving a forecast on prices by using “Auto Regressive Integrated Moving Average and Linear Regression Algorithms” combined with the options chain data and other technical user interfaces. In this way, we believe that moving averages, Relative Strength Index and Moving Average Convergence Divergence technical indicators are crucial ones from the perspective of a complete study using the framework devised by us. These signals are the typical indicators that option chains can change too; these include implied volatility, open interest and put-call ratios and they will be part of what helps you with stock market prediction. Analysing trends in data can be fun! When we use linear regression on what happened before, we get some useful technical. ARIMA helps us predict and forecast linear patterns in the data over time. To check how well our model works, we look at some accuracy metrics. These include mean absolute error, root mean square error, and mean square error. The correctness of our approach is quite good. We attain, lets us take an example, a prophesy estimation of nearly about 92.10%, with an MSE of 0.08, an RMSE of 0.28, and an MAE of 0.199. Thus, we can increase our insight of market trends and make more realistic financial forecasts by using option chain data and technical algorithm and combining ARIMA and linear regression.