Abstract: Portfolio Optimization is to evolve models to compute an optimal proportion of capital for investing with respects to the assets in the portfolio. Portfolio optimization covers a wide range of financial assets, such as stocks, funds, bonds, commodities, currencies and loans, whereas similar concepts and ideas are also applicable to nonfinancial portfolios. Asset price prediction is an important challenge in portfolio optimization. This project utilizes Support Vector Machines, a Machine learning algorithm for asset price prediction. SVM is very accurate and gives better results compared to other techniques. This project is mainly concentrated on predicting asset price followed by portfolio optimization considering the risk and return associated with each and every asset using R programming.