Reducing the energy demand in buildings remains an urgent need, calling for innovative solutions. Additive Manufacturing (AM) is an evolving and rapidly advancing technology, which enables the manufacture of prefabricated and on-site components. 3D-printed (3DP) building components with optimized thermal performance present a promising approach for highly efficient envelope fabrication. However, while structural performance has been extensively studied, thermal behaviour requires further investigation. This study investigates how optimizing geometry and material composition can enhance the thermal properties of 3DP building envelope components. Algorithmic and parametric design was employed to generate and evaluate geometrical variations iteratively. Using Colibri for Rhinoceros, thermal transfer data for multiple design alternatives was analysed to identify the best-performing configurations based on heat path length and void fraction. Afterwards, the selected geometry was manufactured employing Liquid Deposition Modelling (LDM), using a clay mixture stabilized with Calcium Hydroxide (Ca(OH)2). Thermal conductivity of three different admixtures was measured employing a Heat Flux Meter (HFM) apparatus. Results indicate that adding Ca(OH)2 to the mixture can reduce thermal conductivity by up to 58%. Furthermore, optimizing the wall infill shape and void fraction, can lead to a thermal transmittance of 2.35 W/m2K (air cavities) and 1.33 W/m2K (aerogel filled cavities) for a 100 mm thick wall sample. The proposed methodology is replicable and scalable, facilitating the design and assessment of innovative 3DP building components. This work integrates computational design and AM, offering advantages such as improved thermal performance, use of locally available materials, and optimized resources efficiency, costs, and time in construction.