Engineering and intelligent systems increasingly require decision-making under heterogeneous evidence. These sources include multi-source data, predictive models, competing objectives, feasibility constraints, and uncertainty. Evolutionary algorithms (EAs) are widely used in such settings, yet the literature is typically organised by algorithmic lineage, which obscures how and where information is fused within the evolutionary process. This survey reframes EAs as adaptive information fusion architectures. We introduce a fusion-centric taxonomy spanning data-level integration of heterogeneous observations, model/feature-level integration through surrogate and learning components, objective-level integration through multi- and many-objective formulations, constraint handling as feasibility-signal integration, and decision-level integration through ensembles, distributed (island) evolution, and multi-run aggregation. Building on this perspective, we synthesise major EA families according to their dominant fusion mechanisms and review fusion-driven applications across structural and mechanical design, energy and smart grids, robotics and control, communications, healthcare engineering, and neural architecture search. To complement this qualitative synthesis, we propose lightweight quantitative indicators—integration depth, integration diversity, and decision outcome entropy—to characterise algorithm–fusion–domain alignment and to identify recurring success and failure modes, including surrogate bias, over-aggregation, and fusion over-complexity. We conclude with practical design guidelines and discuss emerging directions, including federated evolutionary fusion and reliability considerations in learning- and large language model (LLM)-assisted fusion.