Financial institutions need accurate risk management to stay competitive and keep the economy stable. A new method called SVR-GARCH-KDE hybrid is proposed to forecast Value-at-Risk (VaR) by considering non-linear and non-parametric factors. This approach uses support vector regression (SVR) for mean and volatility modeling, GARCH for volatility, and kernel density estimation (KDE) for VaR calculation. The hybrid model is compared to traditional models and performs well, especially in outperforming models using a normal distribution. This new method allows for flexible tail shapes in profit and loss distribution, making it adaptable to various market conditions.