This study investigated the stabilization of fine-grained soil from the Indo-Gangetic plain using nano-silica (NS) and predicted the unconfined compressive strength (UCS) using advanced machine learning techniques. Experimental investigations were conducted on 118 UCS samples with NS contents varying from 0.5 to 4%. The results showed significant improvements in the soil plasticity, compaction characteristics, and UCS with NS incorporation. NS acted as a reinforcing agent, filling void spaces and improving interlocking between soil particles, leading to increased maximum dry density, reduced optimum moisture content, and notable improvements in the UCS. Microstructure analysis revealed the positive impact of NS on soil properties, attributed to enhanced durability, reduced swell strains, and improved strength due to the synergistic effects of NS particles. Furthermore, five innovative hybridized models based on artificial neural networks (ANN) and nature-inspired optimization algorithms were developed to predict the UCS of NS-stabilized fine-grained soils. The models demonstrated high accuracy, with R2 values exceeding 0.96 and 0.89 for the training and testing dataset. The ANN-Firefly algorithm (ANN-FF) model emerged as the most proficient predictor. This study highlights the importance of input parameters in model development and suggests that further research should focus on expanding experimental data to enhance model flexibility. The proposed approach offers significant implications for cost and time savings in experimental sample preparation and demonstrates the high capability of ANN to determine optimal values for soil stabilization techniques in the Indo-Gangetic plains.