Urban areas face increasing challenges in constructing resilient infrastructure on weak or unstable soils, especially amid the impacts of climate change and rapid urbanization. This study introduces an innovative AI-driven framework that integrates advanced hybrid deep learning architectures namely Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Transformer models for accurately predicting the Unconfined Compressive Strength (UCS) of Nano-silica (NS) stabilized soils. A key innovation of this research lies in the development of a novel CNN-Transformer hybrid model, which outperforms traditional and standalone AI models, achieving an R² of 0.97 and RMSE of 0.22 reducing prediction error by over 49%. Robustness was further validated using a 10,000-iteration Monte Carlo simulation. In addition to predictive modeling, this study pioneers a comparative Life Cycle Assessment (LCA) between NS and cement-based stabilization, revealing that NS reduces CO₂ emissions by 55%, lowers energy consumption by 73%, and improves material efficiency. Furthermore, a user-friendly Graphical User Interface (GUI) tool has been developed, enabling real-time optimization of NS dosage for practical implementation in urban projects. This research not only contributes a high-performance predictive tool but also supports sustainable construction practices, aligning with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production).