In the context of simulation-based optimisation, this paper reviews recent work related to the role of metaheuristics, matheuristics (combinations of exact optimisation methods with metaheuristics), simheuristics (hybridisation of simulation with metaheuristics), biased-randomised heuristics for ‘agile’ optimisation via parallel computing, and learnheuristics (combination of statistical/machine learning with metaheuristics) to deal with NP-hard and large-scale optimisation problems in areas such as transport and logistics, manufacturing and production, smart cities, telecommunication networks, finance and insurance, sustainable energy consumption, health care, military and defence, e-marketing, or bioinformatics. The manuscript provides the main related concepts and updated references that illustrate the applications of these hybrid optimisation–simulation–learning approaches in solving rich and real-life challenges under dynamic and uncertainty scenarios. A numerical analysis is also included to illustrate the benefits that these approaches can offer across different application fields. Finally, this work concludes by highlighting open research lines on the combination of these methodologies to extend the concept of simulation-based optimisation.