Shape Memory Polymers (SMPs) are the class of smart materials that show the ability to memorize different shapes and regain original shape. It finds a wide range of applications in the field of biomedical, aeronautical and aerospace. The conventional method for utilising the shape memory property of SMPs is hot programming where a SMP is heated above its glass transition temperature (Tg), then deformed, and finally cooled to obtain a desired shape. It reverts to initial or the memorised-shape upon heating. Conventional hot programming method is an energy consuming process when it comes to large-structure applications. In addition, structural integrity of the system is compromised when SMP is heated at temperatures above Tg. Recent advances in the programming of SMP have shown promising results of a new programming method where the SMP is deformed at temperatures below Tg and then stress-relaxed to impart the desired shape. It not only reduces the complexity of the system but also improves recovery stress of SMP. The choice of material and programming condition plays a key role in achieving best shape memorising capability. This research work aims at developing a candidate SMP for unconventional programming and establishing optimum programming parameters for the desired shape memory properties. Further, graphene nanoplateletes are used to develop a Shape Memory Polymer Nanocomposites (SMPNs) capable of heating on passing electric current for shape recovery. Moreover, there is a lack of study on deterioration of shape memory and mechanical properties due to repeated large deformation programming strains for unconventional programming techniques. A suitable constitutive model that can predict large deformations below Tg, shape fixing and shape recovery phenomenon has been incorporated to simulate the behaviour of material.