Purpose of ReviewForests are integral to global ecological stability, climate regulation, and economic resilience. They function as major carbon sinks, act as biodiversity reservoirs, and provide ecosystem services. Accurately modeling forest growth is essential to predict ecosystem responses to climate change and optimize ecosystem services. However, predicting forest growth remains challenging due to complex interactions between ecological processes, external drivers like climate change, and intrinsic dynamics, such as legacy effects and emergent properties, that influence forest responses over time.This work offers a detailed examination of theories in forest growth modeling, with a focus on emergent approaches as implemented in 18 forest growth models, which vary in their approaches and goals. Recent FindingsForest modeling requires a deep understanding of forest growth theories driven by multiple, often interacting, processes. Our findings reveal distinct model clusters with varying process integrations and complexity, ranging from stand-level to terrestrial ecosystem models. Additionally, we highlight the trade-offs between model detail and scalability.SummaryOur review showcases multiple theories, such as Functional Balance, Local Determination of Growth, and Optimality Principles of forest growth, thus providing a synthetic overview of the main frameworks for resource allocation in plants. As multiple studies emphasize the importance of integrating different and recent theories to better capture growth dynamics, we build on a state-of-the-art multi-modelling comparison to discuss what the implications of different theories might be at different temporal and spatial resolutions. Finally, we explore how emerging technologies, such as machine learning, can enhance predictive accuracy and help address current modeling limitations.