Fall from a height trauma is characterized by a multiplicity of injuries, related to multiple factors. The height of the fall is the factor that most influences the kinetic energy of the body and appears to be one of the factors that most affects the extent of injury. The purpose of this work is to evaluate, through machine learning algorithms, whether the autopsy injury pattern can be useful in estimating fall height. 455 victims of falls from a height which underwent a complete autopsy were retrospectively analyzed. The cases were enlisted by dividing them into 7 groups according to the height of the fall: 6 or less meters; 9 m, 12 m, 15 m, 18 m, 21 m, 24 m or more. Autoptic data were registered through the use of a previously published visceral and skeletal table. A total of 25 descriptors were used. Reduction of values in the range, standard and robust scaling were used as preprocessing methods. Principal Component Analysis, Single Value Decomposition and Independent Component Analysis were applied for dimensionality reduction. Cross validation was performed with 5 internal and external folds to ensure the validity of the results. The learning algorithms that generated the best models were Linear Regression, Support Vector Regressor, Kernel Ridge, Decision trees and Random forests. The best mean absolute error was 4.58 ± 1.28 m when dimensionality reduction was applied. Without any dimensionality reduction, the best result was 4.37 ± 1.27 m, suggesting a good performance of the proposed algorithms, with better performance when dimensionality is not automatically reduced.