103A comprehensive review of mathematical models for forecasting population growth: exploring current frontiers

Population growth forecasting plays a critical role in addressing global challenges such as resource allocation, urban development, and economic planning. This paper offers a systematic review of mathematical models employed to predict population growth, focusing on their evolution, strengths, limitations, and real-world applications. We analyze a wide spectrum of models, from classical approaches like the Malthusian and logistic growth models to advanced techniques such as the von Bertalanffy and multifactorial models. Special emphasis is placed on recent advancements, including stochastic models, agent-based simulations, and machine learning methods, which enhance the predictive capabilities of traditional approaches. By exploring both the theoretical underpinnings and empirical applications of these models this review highlights emerging trends in population dynamics and the adaptability of models to shifting demographic, environmental, and social contexts. The paper concludes by discussing future directions in population growth modeling, stressing the importance of interdisciplinary approaches and the integration of real-time data for improving forecasting accuracy. This review aims to serve as a comprehensive resource for researchers and policymakers navigating the evolving landscape of population forecasting.