Optimized Machine Learning Models for Poverty Detection: A Scientific Review of Multidimensional Approaches

This paper enhances the discussion on machine learning (ML) models for poverty detection by introducing empirical validation, comparative performance analysis, and practical deployment strategies. We validate the proposed Optimized Machine Learning Model (OMLM) through experiments on real-world datasets. A comparative study against existing poverty detection models, including logistic regression, decision trees, and convolutional neural networks (CNNs), highlights OMLM’s superior adaptability and accuracy. The paper further explores data limitations, computational efficiency, and regional performance variations. Finally, a novel optimization technique, combining Genetic Algorithms (GA) with Reinforcement Learning (RL), is introduced to refine predictive accuracy and real-time adaptability. Practical implementation details, including data processing pipelines, cloud-based deployment, and integration into governmental policy frameworks, are discussed to enhance the model’s real-world applicability. This study contributes to advancing ML applications in poverty detection, reinforcing its role in data-driven policymaking and targeted socio-economic interventions.