ABSTRACT Fisheries management increasingly demands robust forecasting tools to address growing environmental variability, anthropogenic pressures and complex ecological dynamics. This review systematically examines innovative modelling and forecasting approaches in fisheries, focusing on their descriptions, applications, strengths and limitations and comparative performance based on quantitative and qualitative evaluation criteria. Drawing on major scientific databases with studies published between 2000 and 2023, the review covers a broad spectrum of models, including Population Dynamics Models, Ecosystem Models, Statistical and Time Series Models, Machine Learning Models, Bioeconomic Models, Simulation Models, Spatial and Habitat Models and other emerging approaches. Historically, fisheries forecasting evolved from basic observational methods to advanced computational and statistical techniques. Conventional models such as surplus production and age‐structured models remain valuable for certain stable systems with limited data. However, ecosystem‐based models (e.g., Ecopath with Ecosim, Atlantis) and machine learning techniques (e.g., neural networks, random forests, deep learning) offer enhanced adaptability and predictive accuracy, particularly under dynamic and uncertain conditions. Despite these advances, challenges persist, including data scarcity, difficulties in model validation and integration of socio‐economic and climate‐related variables. Hybrid models that combine ecological, economic and social factors, especially those incorporating real‐time data and artificial intelligence, show promise for improving fisheries forecasting. Progress in this field will require interdisciplinary collaboration, enhanced data systems and stronger policy integration to ensure sustainable fisheries management. This review provides a structured framework to guide researchers and decision‐makers in selecting and developing more adaptive, accurate and actionable forecasting tools in the face of global environmental change.