Government bond yield forecasting is central to monetary policy analysis, portfolio allocation, and risk management. This study reviews prior econometric, machine-learning, and hybrid approaches for yield prediction and identifies non-stationarity as a persistent challenge in emerging-market sovereign yields. Daily 10-year Indian government bond yields (2015–2024) are analysed; stationarity diagnostics motivate both level-based and differenced formulations. Six forecasting approaches are implemented, including linear baselines, tree-based learning, residual-learning ensembles, a hybrid ARIMA–LSTM, a differenced-series LSTM, and a proposed Two-Head LSTM that jointly learns level and first-difference dynamics. Models are evaluated on a strictly held-out test block (250 observations) using MAE, RMSE, and directional accuracy, supported by visual diagnostics. Results show that hybrid and multi-output designs outperform classical and single-objective baselines; the Two-Head LSTM achieves the lowest error (MAE 0.18; RMSE 0.24) and highest directional accuracy (89.5%). The findings indicate that architectures explicitly modelling both I(1) and I(0) components can substantially improve forecasting accuracy for Indian sovereign yields.