Background: India&rsquo;s minimum support price (MSP) regime creates an administered floor for wholesale (mandi) prices, making accurate price forecasting the analytical foundation for farmer revenue-risk assessment, government procurement planning and the design of price- or revenue-based crop insurance instruments. Wheat and maize present a sharp institutional contrast: wheat is procurement-binding through the Food Corporation of India, while maize receives little procurement support and its price is left largely to private trade. This study benchmarks machine-learning and classical time-series forecasters for the monthly modal mandi price of both crops across a structurally distinct, post-pandemic hold-out period and defines, in measurable terms, what it means for such a series to be MSP-anchored. 
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Methods: This analysis was carried out at Sri Sathya Sai Institute of Higher Learning (SSSIHL), Prasanthi Nilayam, Andhra Pradesh, India, during 2026. Daily AGMARKNET records for wheat and maize (April 2008-March 2025) were aggregated into 204 monthly All-India modal-price observations per crop-the mean of daily modal prices-with the corresponding MSP reported for reference. Eight algorithms-ridge regression, support vector regression, random forest, extremely randomised trees, gradient boosting, XGBoost, ARIMA and SARIMA-together with a na&iuml;ve persistence benchmark, were trained on 177 months (April 2008-December 2022) and evaluated out-of-sample on 27 held-out months (January 2023-March 2025) using the mean absolute error, root mean squared error and mean absolute percentage error (MAPE). 
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Result: Ridge regression on lagged prices and a month indicator dominated every fitted ensemble and, with ARIMA and SARIMA evaluated as static multi-step forecasts, every time-series competitor for both crops, achieving a MAPE of 2.75 per cent for wheat (mean absolute error ₹ 68.97/quintal) and 2.59 per cent for maize (₹ 55.97/quintal). Tree-based learners and support vector regression deteriorated sharply on the trending test window; SARIMA was competitive only for maize. Against na&iuml;ve persistence, however, ridge&rsquo;s edge was modest for wheat and mixed for maize-persistence matched ridge on MAE and MAPE and narrowly beat it on RMSE-indicating ridge&rsquo;s advantage lies chiefly over the more flexible learners. This is attributed to the near-unit autocorrelation of MSP-anchored series and the inability of tree ensembles to extrapolate beyond their training range, consistent with, though not proving, the institutional asymmetry between procurement-stabilised wheat and procurement-absent maize. These forecasts form the price engine for companion volume-forecasting and procurement-liability studies within the broader actuarial data-science revenue-protection framework.

Forecasting Minimum Support Price-anchored Mandi Prices for Wheat and Maize in India: A Comparative Machine-learning and Time-series Framework