The financial Analysis of stock market exchange prediction is the confirmation of participating to decide the future association of a group stock or other currency product exchange on a money related business. The effective expectation of a stock's future cost will expand speculator's benefits. This paper proposes a machine learning model to foresee financial exchange cost. The proposed calculation incorporates Particle Swarm Advancement and least square help vector machine. Particle Swarm Advancement calculation chooses best boundaries for vector machine to maintain a strategy based distance from over-fitting and neighbour minima issues and improve expectation exact result. The access of proposed approach was applied and utilizing thirteen benchmark financials datasets and copied neural system with Levenberg-Marquardt theory. The outcomes indicated that the proposed model has better forecast exactness and the capability of PSO calculation in upgrading vector machine. The principle target of writing this paper is to show the location of the best model to expect the estimation of the stock exchange. Continuing the way towards consideration of various rules and algorithms which must be included, we research on the methods like improper woodland, vector machine which were not in wrong used completely. I am going to represent and a kind of survey which is a successfully achievable algorithm to previse the stock market development strategy with a high accuracy. The main objective we have considered is the data of the financial exchange costs from earlier year.