This project presents a comprehensive analysis of the microeconomic-level household income sufficiency indicator through the application of advanced machine learning techniques. The core objective is to develop an intelligent system capable of accurately predicting household expenditures and assessing income sufficiency at a microeconomic scale. To achieve this, a N-tiered modeling approach is employed. The primary model is a novel hybrid deep learning architecture designed specifically for predicting household expenditures. This model integrates the strengths of both a convolutional neural network, a multi-layer perceptron, and XGBoost, thereby enhancing the accuracy and robustness of expenditure predictions. Complementing this, a secondary model is implemented to predict the household income sufficiency indicator. This model not only processes household input data but also integrates Explainable Artificial Intelligence (XAI) techniques. The inclusion of XAI enhances the transparency, interpretability, explainability, and trustworthiness of the model’s predictions, enabling stakeholders to understand the reasoning behind the sufficiency assessments. Such interpretability and explainability are essential for householders who require clear and actionable insights for effective decision-making. Together, the dual-model framework provides a practical and scalable solution for understanding and addressing income adequacy at the household level, significantly contributing to socio-economic planning and efforts to reduce household income insufficiency through data-driven intelligence and explainable outcomes.