3D printing has emerged as a promising tool for on-demand and rapid fabrication of materials. The field of soft material printing typically utilises inks that exhibit viscoelastic properties with elastic moduli in the kPa – MPa range, such as hydrogels and elastomers. Although soft material printing has been frequently used for creating biomimetic mini tissues, its ability to imitate organ functions as a direct result of organ anatomy is yet to be fully realised, and continued innovation in printing method and flexible machinery are needed to drive the field forward. My PhD thesis focuses on advancing the field of soft material printing. Specifically, there are three main scopes in my work. Firstly, I developed an affordable and fully customisable extrusion-based printing platform for soft materials. The platform is equipped with multiple printheads for heterogeneous construct printing, and heating systems and a UV module for tuning the material rheology during and after printing. A detailed assembly instruction and the software design are provided, hence new users can facilely replicate the platform and contribute to the continued development of the platform. In summary, it is anticipated that this entirely hackable platform can facilitate the widespread adoption of the technology, overcoming the cost and flexibility barriers presented in commercial systems. Secondly, to realise the potential of 3D printing for imitating physiological phenomena related to anatomical structures, I created 3D printed cochleae that exhibit similar electro-anatomical features resembling human cochleae. These biomimetic cochlear models were integrated with machine learning to advance clinical predictions of ‘current spread’ for cochlear implant (CI) patients. The co-modelling framework demonstrated autonomous predictions of patient electric field imaging profile or cochlear geometry, unfolded the electro-anatomical factors causing CI stimulus spread, assisted on-demand printing for CI testing, and inferred patients’ in vivo cochlear tissue resistivity by CI telemetry. This framework might facilitate physical modelling and digital twin innovations for neuromodulation implants in healthcare. Lastly, I demonstrate the high flexibility and versatile functionalities of the custom-made 3D extrusion printing platform. Apart from 3D CAD models, the standard geometry input used in 3D printing, the platform accepts unconventional geometry inputs to suit different needs, including coordinates, equations and pictures. Advanced operations, such as liquid dispensing, printing with variable speed and non-planar printing, are permitted with the platform. With the aid of support baths, heating and UV tools, a wide variety of soft materials, including naturally derived hydrogels, pH-responsive hydrogels and elastomers, were successfully printed using the platform. Overall, the perspective provided in this work might guide new users to efficiently design printing processes for soft materials that do not possess suitable rheological and mechanical properties for creating 3D structures with conventional extrusion methods.