To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent radiotherapy in the pre-CT era, only 2D radiographs were acquired, thus 3D dose distributions must be reconstructed from limited information. State-of-the-art methods achieve this by using 3D surrogate anatomies. These can lack personalization and lead to coarse reconstructions. We present and validate a surrogate-free dose reconstruction method based on Machine Learning (ML). Abdominal planning CTs ($n$=142) of recently-treated childhood cancer patients were gathered, their organs at risk were segmented, and 300 artificial Wilms' tumor plans were sampled automatically. Each artificial plan was automatically emulated on the 142 CTs, resulting in 42,600 3D dose distributions from which dose-volume metrics were derived. Anatomical features were extracted from digitally reconstructed radiographs simulated from the CTs to resemble historical radiographs. Further, patient and radiotherapy plan features typically available from historical treatment records were collected. An evolutionary ML algorithm was then used to link features to dose-volume metrics. Besides 5-fold cross-validation, a further evaluation was done on an independent dataset of five CTs each associated with two clinical plans. Cross-validation resulted in Mean Absolute Errors (MAEs) $\leq$0.6 Gy for organs completely inside or outside the field. For organs positioned at the edge of the field, MAEs $\leq$1.7 Gy for D$_{mean}$, $\leq$2.9 Gy for D$_{2cc}$, and $\leq$13% for V$_{5Gy}$ and V$_{10Gy}$, were obtained, without systematic bias. Similar results were found for the independent dataset. Our novel, ML-based organ dose reconstruction method is not only accurate but also efficient, as the setup of a surrogate is no longer needed.