The rapid pace of urbanization has significantly increased challenges in managing municipal solid waste (MSW), especially in the collection and transportation of kitchen waste. As urban populations and consumption rise, the need for effective kitchen waste management becomes more complex. In this study, we propose an efficient kitchen waste collection system with seasonal flexibility to reduce overall collection costs. To analyze the spatiotemporal variations in kitchen waste generation, we integrated monthly MSW generation, spatial distribution, and separation rates to predict the seasonal spatial distribution of kitchen waste at a 500 m × 500 m (0.25 km²) resolution. First, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model with seasonal differencing was applied to characterize monthly MSW generation in Beijing over ten years (2010—2019). The results show that MSW generation is lowest in January–February (off-season) and peaks in July–August (peak season). The average daily MSW generation ratios for the off-season, peak season, and normal season are 88∶107∶100 (normal season = 100). This seasonal variability underscores the need for adaptive collection systems. Next, we developed a ridge regression model to examine how district-level socioeconomic and demographic factors, as well as point-of-interest (POI) distributions, influence MSW generation. By combining these predictors with post-sorting kitchen waste separation rates, the model estimated the seasonal spatial distribution of kitchen waste in 2021 across 19,953 grid cells (0.25 km² each). Spatial validation for the off-season, peak season, and normal seasons in 2021 yielded R² values greater than 0.98, indicating stable spatiotemporal extrapolation capability. Using this approach, we further projected the spatiotemporal distribution of kitchen waste in 2025. Through location-allocation analysis and a multi-route Vehicle Routing Problem (VRP) optimization, we derived cost-optimal daily collection routes for each season. The analysis indicates that the daily total collection costs for the normal season, off-season, and peak season are approximately in the ratio of 1.00∶1.08∶0.90. These seasonal cost variations highlight the sensitivity of the sanitation system to seasonal dynamics and the necessity of flexible collection strategies. This study provides a feasible method for optimizing kitchen waste collection in Beijing and offers insights for intelligent and sustainable kitchen waste management under source-separation policies in other cities. The findings serve as a reference for urban areas facing similar challenges and demonstrate that flexible, data-driven strategies can improve the efficiency and sustainability of kitchen waste management systems. Finally, we outline directions for future work, including integrating real-time data and advanced machine learning models to further enhance adaptability and sustainability.