This article reviews the current state of climate-related migration forecasting models, based on an analysis of thirty recent models. It discusses the key characteristics, strengths, and weaknesses of different modeling approaches, including gravity, radiation, agent-based, systems dynamics, and statistical extrapolation models. The article highlights that forecasting models are not yet able to provide reliable numerical estimates of future climate-related migration. Instead, models are best used as tools to consider a range of possible futures, to explore systems dynamics, to test theories or potential policy effects. The article also considers the policy and research implications of these findings, including the need for improved migration data collection, enhanced interdisciplinary collaboration, and scenarios-based planning. Climate-related migration is defined as the movement of people due to sudden or progressive environmental changes caused by climate change. It includes both forced and voluntary migration, internal and international, temporary and permanent. The article discusses the challenges in defining and quantifying "climate migration," and the different types of forecasting models used to predict future migration trends. These models include exposure models, agent-based models, gravity models, radiation models, statistical extrapolation models, systems dynamics models, computable general equilibrium models, and integrated assessment models. The article finds that the field of climate-related migration forecasting remains in its infancy, and numerical estimates of future climate-related migration are best taken as speculative. Although significant advancements in modeling have been made, several key limitations, both empirical and conceptual, constrain the real-world applicability of their projections. These limitations include the lack of reliable data on migration flows in many countries most vulnerable to climate change; a theoretical and empirical overemphasis of climate-related drivers of migration that neglects other political, economic, demographic, cultural, and technological determinants of migration and immobility; and a related dearth of reliable data on these non-climate related variables. The article also discusses the different types of climate-related migration forecasting models, their strengths and weaknesses, and the challenges in forecasting climate-related migration. It highlights the importance of considering the complex interactions between climate change, migration, and development, and the need for improved data collection, interdisciplinary collaboration, and scenarios-based planning to better understand and predict future climate-related migration.This article reviews the current state of climate-related migration forecasting models, based on an analysis of thirty recent models. It discusses the key characteristics, strengths, and weaknesses of different modeling approaches, including gravity, radiation, agent-based, systems dynamics, and statistical extrapolation models. The article highlights that forecasting models are not yet able to provide reliable numerical estimates of future climate-related migration. Instead, models are best used as tools to consider a range of possible futures, to explore systems dynamics, to test theories or potential policy effects. The article also considers the policy and research implications of these findings, including the need for improved migration data collection, enhanced interdisciplinary collaboration, and scenarios-based planning. Climate-related migration is defined as the movement of people due to sudden or progressive environmental changes caused by climate change. It includes both forced and voluntary migration, internal and international, temporary and permanent. The article discusses the challenges in defining and quantifying "climate migration," and the different types of forecasting models used to predict future migration trends. These models include exposure models, agent-based models, gravity models, radiation models, statistical extrapolation models, systems dynamics models, computable general equilibrium models, and integrated assessment models. The article finds that the field of climate-related migration forecasting remains in its infancy, and numerical estimates of future climate-related migration are best taken as speculative. Although significant advancements in modeling have been made, several key limitations, both empirical and conceptual, constrain the real-world applicability of their projections. These limitations include the lack of reliable data on migration flows in many countries most vulnerable to climate change; a theoretical and empirical overemphasis of climate-related drivers of migration that neglects other political, economic, demographic, cultural, and technological determinants of migration and immobility; and a related dearth of reliable data on these non-climate related variables. The article also discusses the different types of climate-related migration forecasting models, their strengths and weaknesses, and the challenges in forecasting climate-related migration. It highlights the importance of considering the complex interactions between climate change, migration, and development, and the need for improved data collection, interdisciplinary collaboration, and scenarios-based planning to better understand and predict future climate-related migration.