Government bond yield forecasting occupies a central place infinancialeconomics,withdirectimplicationsformonetarypolicy,port- folio allocation, and risk control. This paper undertakes a detailed empir- ical assessment of a wide spectrum of forecasting techniques—including classical, machine-learning, and advanced hybrid models—using the 10- Year Indian Government Bond Yield as the focal case. We benchmark a simple predictive model against a suite of advanced approaches, includ- ing an ensemble with residual Multi-Layer Perceptron (MLP), a hybrid ARIMA-LSTM model, an LSTM on differenced yields (∆-Yields), and a novel Two-Head LSTM architecture. Models are evaluated on a test sam- ple of 250 observations using both quantitative metrics (MAE, RMSE, DA)andqualitativevisualanalysis.Resultsdemonstratethathybridand multi-output models significantly outperform traditional benchmarks. The novel Two-Head LSTM, which simultaneously learns to predict the yield level and its first difference, achieves the lowest error (MAE: 0.18, RMSE: 0.24) and highest directional accuracy (89.5%), almost perfectly capturing the complex dynamics and turning points of the yield series. The Ensemble + Residual MLP model also proves highly effective. This study concludes that architectures explicitly designed to model both the integrated (I(1)) and stationary (I(0)) components of a financial time se- ries offer a substantial and statistically significant improvement in fore- casting accuracy for emerging market government bonds.