Runoff estimation in India faces challenges due to diverse climate zones, complex physiographic conditions, and variable rainfall patterns, limiting traditional hydrological models and prompting exploration of advanced deep learning methods for improved streamflow prediction. Existing deep learning hydrological models struggle to estimate discharge at ungauged sites. In this study, we tested eight different deep learning models—four recurrent neural networks (GRU, CudaLSTM, EALSTM, ARLSTM) and four attention-based architectures (Transformer, Informer, Reformer, Linformer)—across 144 watersheds in the Indian subcontinent (ISC). Our training and testing datasets combined meteorological forcing, catchment attributes, and observed discharge records. According to the results, ARLSTM improved prediction accuracy, achieving a median Nash–Sutcliffe Efficiency (NSE) of 0.71 on test basins. ARLSTM performs exceptionally well in specific regions: tropical monsoon areas (median NSE = 0.849), semi-arid regions (median NSE = 0.586), monsoon-influenced subtropical zones (NSE = 0.688), and tropical wet–dry climates (NSE = 0.539), especially in arid zones where traditional hydrological models often struggle. The assessments of high- and low-flow frequencies and durations, mean discharge, and runoff ratios underscore ARLSTM's capability to capture both extreme and average flow conditions. ARLSTM’s reliance on lagged streamflow limits its use in ungauged basins. To address this issue, we developed a novel deep learning architecture, Ungauged Basin LSTM (UBLSTM), to predict the runoff values for any ungauged basin in India. UBLSTM matches the performance of ARLSTM, making it a better choice for areas in India that lack sufficient data or have ungauged basins across various climate zones.