Dry-cured ham may be contaminated with aflatoxin B₁ (AFB₁) produced by Aspergillus spp. Temperature and water activity (a_w) are two key parameters that affect both ham ripening and AFB₁ production. The objective of this study was to predict AFB₁ production by Aspergillus parasiticus and Aspergillus flavus strains in conditions related to dry-cured ham ripening using data mining techniques. J48 decision tree, isotonic regression (IR), and multiple linear regression (MLR) were tested to (a) classify and predict AFB₁ concentration as a function of different days, temperatures and a_w values and (b) predict the beginning of AFB₁ production as a function of different temperatures and a_w values. For this, a model system based on a dry-cured ham-based medium was used. The percentage of correct classification was higher than 75%. R values to predict the concentration of AFB₁ when applying MLR were 0.81, being higher than those obtained after using IR. The models developed were validated with experimental data obtained after inoculating samples of dry-cured ham with two aflatoxigenic strains. The predicted AFB₁ concentration showed correlation coefficients ≥0.74 and prediction errors ≤0.38, confirming the feasibility of the prediction equations obtained. This information may help to make informed decisions to minimise the hazard posed by AFB₁ in dry-cured ham.