This paper presents a Bayesian framework for estimating individualized treatment effects (ITE) in high-dimensional observational data. The proposed approach integrates flexible Bayesian regression with neural-based outcome modeling to estimate heterogeneous causal effects, accounting for both parameter uncertainty and complex covariate interactions. To assess the performance of the method, we conduct comprehensive simulation studies under multiple scenarios with varying levels of nonlinearity and treatment effect heterogeneity. Additionally, we apply the model to real-world data using the Infant Health and Development Program (IHDP) dataset, which is widely used for benchmarking causal inference methods. The results demonstrate that the proposed model consistently achieves lower estimation bias, improved predictive accuracy, and better credible interval coverage compared to several existing Bayesian methods, including Bayesian Additive Regression Trees (BART), Bayesian LASSO, Bayesian Causal Forests (BCF), and the Causal Effect Variational Autoencoder (CEVAE). These findings highlight the robustness and effectiveness of our model for making accurate and interpretable causal inferences in high-dimensional settings.