A machine learning approach for extracting information from organic photovoltaic (OPV) solar cell data is presented. A database consisting of 1850 entries of device characteristics, performance and stability data is utilised and a sequential minimal optimisation regression (SMOreg) model is employed as a means of determining the most influential factors governing the solar cell stability and power conversion efficiency (PCE). This is achieved through the analysis of the acquired SMOreg model in terms of the attribute weights. Significantly, the analysis presented allows for identification of materials which could lead to improvements in stability and PCE for each thin film in the device architecture, as well as highlighting the role of different stress factors in the degradation of OPVs. It is found that, for tests conducted under ISOS-L protocols the choice of light spectrum and the active layer material significantly govern the stability, whilst for tests conducted under ISOS-D protocols, the primary attributes are material and encapsulation dependent. The reported approach affords a rapid and efficient method of applying machine learning to enable material identification that possess the best stability and performance. Ultimately, researchers and industries will be able to obtain invaluable information for developing future OPV technologies so that can be realised in a significantly shorter period by reducing the need for time-consuming experimentation and optimisation.