A common measurement for studying the effect of burning fossil fuels on weather patterns is the level of carbon dioxide (CO2) concentration in the atmosphere. Some scientists believe the upward trend in CO2 levels could cause atmospheric temperatures to rise, polar ice caps to melt, and climates of different regions of the earth to change radically. Since this data has cyclical phenomena, as well as an upward growth trend, nonlinear fitting techniques are appropriate, but can require added effort when compared to fitting a linear model. In this project, I adopt the annual global Carbon-dioxide (CO2) processed data from National Oceanic and Atmospheric Administration Research (NOAA/ESRL), using MATLAB for the analysis and statistics. The aim is to use curve fitting methods to project time series analysis in yearly average atmospheric CO2 data, to examine goodness-of-fit statistics and output arguments, filtering of residuals using the filtering method to transform the data into the frequency domain and predict future CO2 cycles with confidence intervals against the fit and the data. The values of the predicted CO2 from year 2020 to 2050 ranges from 411 – 500 ppm, which project massive uncertainties in CO2 concentrations due to the range of possible climate - carbon cycle feedback in the nearest future.