This research investigates household food consumptionHousehold food consumption habits in Bangladesh, highlighting their socio-economic consequences and predicting patterns with machine learningMachine learning algorithmsAlgorithm. A cleaned dataset including 209,264 items and 31 features was analyzed using data from the Food and AgricultureAgriculture Organization (FAO), combined with data from the Intergovernmental Panel on Climate ChangeClimate change (IPCC) and other sources from 1990 to 2021. Critical elements like urbanization, agricultural methodologies, and demographic trends were analyzed for their impact on food consumption. Six supervised machine learningMachine learning models- Linear Regression (LR), Ridge Regression (RR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest RegressionRandom forest regression (RFR), and Polynomial Regression (PR) were employed, and the models were evaluated using evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R Squared (R2). The results indicate that RFR and LR are the most effective models, achieving an R2 of 0.99, representing greater prediction accuracy than other approaches. This study outperforms prior findings using advanced preprocessing methods and strong modeling frameworks. The findings highlight the effectiveness of predictive modeling in policy formulation, providing insights to address food securityFood security issues and inflationary patterns in Bangladesh. These findings establish a foundation for enhancing dietary diversity, urban food systems, andSustainable agriculture sustainable agricultureAgriculture methods in developing economies.