<p>Biomass gasification is a complex process influenced by various factors such as gasifier operating conditions; biomass feed characteristics, and choice of gasifier design. Conducting experimental studies to understand these interactions can be challenging and expensive due to the multitude of variables involved. To address this, simulation and modeling tools play a crucial role in predicting gasification outcomes. This study employs multivariate analysis tools, namely Principal Component Regression (PCR), Partial Least Square Regression (PLSR), and Artificial Neural Network (ANN), to model and simulate the air gasification of biomass using training dataset (<i>n</i> = 170). The objective of the present analysis is to predict the key product gas characteristics, specifically output gas composition (CO, CO₂, CH₄, H₂, N₂) in volume % and its Lower Heating Value in MJ/Nm³. Training data from various literature sources are utilized, incorporating biomass characteristics such as proximate, ultimate analysis, Higher Heating Value in MJ/Kg, and the operating variable of equivalence ratio (ER). A new set of data (<i>n</i> = 21) excluding the training dataset are used for the model validation. Validation results indicate that, the model predicted parameters are within an error margin of 10%. Evaluation metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and one-tail t-test, are applied to assess the accuracy of the models on the validation dataset. The values of RMSE for PCR, PLSR, and ANN models are (<i>0.47766, 0.46022, 0.12621, 1.81209, 0.67679, 0.14601</i>), (<i>0.20718, 0.49909, 0.06744, 0.96549, 0.6324, 0.07951</i>) and (<i>0.22907, 0.53613, 0.21085, 0.5469, 0.13152, 0.01326</i>) for predicted values, respectively.</p> Graphical abstract <p></p>