The importance of electricity in modern society has become increasingly crucial for everyday life. Along with its importance comes the Monetary aspect of buying and selling power in the 21st century. This thesis focuses on predicting Market Electricity Prices in SCE (Southern California Edison) Territory to help Electric Service Providers (ESPs) maximize profits by analyzing upcoming Market prices to make better business decisions. To forecast Market Electricity prices, this thesis utilizes ARIMA modeling, Time series Regression Modeling, Random Forest Modeling, Exponential Smoothing Techniques and Extreme Gradient Boosting. The various models are trained on over two years of data and forecasted for Long-Term, Short-Term and Day-Ahead Market electricity prices to help ESPs across different time scales. To assess the model performance, Root Mean Square Error (RMSE) is utilized across different models. The overall goal of this thesis is to find the best performing model.