Data-mining based machine learning (ML) method is emerging as a strategy to predict aluminium (Al) alloy properties with the promise of less intensive experimental work. However, ML models for wrought Al alloys are limited due to the difficulty in feature digitalization of the variety of manufacturing processes. Hence, most previous studies were constrained to specific alloy designations, which impeded the applicability of those ML models to broader wrought Al alloys. In the present work, we propose a novel feature engineering, called procedure-oriented decomposition (POD), assisting prediction framework to address the complexity introduced by manufacturing processes for wrought Al alloys. In this model, both chemical compositions and manufacturing processes are integrated as features. Correlation mapping of these features to the wrought Al alloys mechanical properties is established using the support vector regressor (SVR) model. The prediction framework demonstrates a high prediction accuracy and potential to design new alloys.