Structural damage from earthquakes has been assessed using a variety of methodologies, both statistical and, more recently, utilizing Machine Learning (ML) algorithms. The effectiveness of data-driven procedures, even when applied to extremely time-consuming scenarios and data sets that reflect substantial expertise and research, completely depends on the quality of the underlying data. The performance of the intelligent model can also be impacted by a lack of in-depth knowledge and expertise in using complex machine learning architectures. This can also prevent some crucial hyperparameters from being adjusted, which ultimately reduces the algorithm's reliability and generalizability. The present research offers a Bayesian-based semi-supervised Automatic Differentiation Variational Inference (ADVI) deep autoencoder for forecasting seismic damage of R/C buildings. It is a state-of-the-art, intelligent technology that automatically converts the variables in the issue into actual coordinate space using an upgraded ADVI technique. Finally, using a brand-new Adaptive Learning Rate Gradient Algorithm (ALRGA), it chooses a technique in this area that is a function of the changed variables and optimizes its parameters. Using the sophisticated ADVI technique to establish a posterior distribution without having an analytical solution is an upgraded version of the semi-supervised learning method. Estimating seismic damage to buildings is accelerated and greatly simplified by the suggested methodology, which eliminates the computational complexity of the analytical methods. By performing Nonlinear Time History Analyses of 3D R/C structures exposed to 65 earthquakes, a realistic dataset for the model evaluation is produced. The system's strong generalizability and the proposed methodology's detailed convergence stability reveal that it is a valuable method that can outperform other ML algorithms.