Saudi Arabia is expanding domestic food production while seeking higher industrial productivity, resource efficiency and resilience under Vision 2030. Olive and tomato processing are strategically relevant because both depend on seasonal agricultural inflows, time-sensitive quality preservation and capacity-constrained thermal, separation and packaging operations. This review develops a data-driven production planning framework for smart-factory capacity optimisation in Saudi olive and tomato-paste manufacturing. The study synthesises literature published from 2020 to 2025 on food-process control, digital twins, Industry 4.0, predictive maintenance, production scheduling and process analytical technologies, complemented by official Saudi agricultural and industrial evidence. The review distinguishes nominal equipment capacity from effective, quality-constrained and utility-constrained capacity, and shows why seasonal factories require planning models that jointly represent raw-material arrivals, product deterioration, changeovers, sanitation, equipment condition, energy and water availability, yield uncertainty and market demand. The proposed framework combines industrial sensing, manufacturing execution data, short-horizon forecasting, digital-twin simulation, finite-capacity scheduling and feedback from advanced process control. It recommends a rolling planning architecture in which forecasted olive and tomato arrivals are converted into feasible campaigns, bottlenecks are protected by predictive maintenance, and process set-points are coordinated with throughput and quality targets. For Saudi manufacturers, the framework links local agricultural scale with industrial localisation by prioritising interoperable data, measurable overall equipment effectiveness, seasonal scenario planning and staged investment rather than technology acquisition without an operating model. The review concludes that smart capacity optimisation can increase usable throughput, reduce avoidable losses and improve resource productivity, but only when planning, process control, maintenance and quality decisions are integrated around a common data model.