<p>The growing demand for advanced materials in engineering applications necessitates the development of composites with enhanced mechanical properties and machinability. This study explores the mechanical properties and machinability of a novel smart hybrid composite composed of Aluminium-Copper (Al-Cu) alloy reinforced with Silicon Carbide (SiC) and Graphene Nanoplatelets (GNPs). The hybrid composite was fabricated using a stir casting method, ensuring uniform dispersion of reinforcements to achieve superior mechanical characteristics. Mechanical properties, including tensile strength, hardness, and compressive strength, were experimentally determined and analysed. Furthermore, the machinability of the hybrid composite was evaluated using Water Jet Machining (WJM), a non-conventional machining technique known for its precision and versatility in handling advanced materials. Key machining parameters such as kerf width, surface roughness, and material removal rate were optimized to assess the machinability of the developed composite. To enhance the understanding of the relationship between composition, mechanical properties, and machinability, a Machine Learning (ML) model was developed using Artificial Neuron Network, Random Forest Regressor and Decision Tree algorithms. The ML model predicted the outcomes of mechanical testing and machining, providing a reliable framework for optimizing composite material design. The results reveal that the addition of SiC and GNPs significantly improves the composite's mechanical properties while maintaining favourable machinability characteristics under WJM. This study demonstrates the potential of smart hybrid composites in engineering applications where both high performance and efficient machinability are essential, and it highlights the effectiveness of machine learning in optimizing material development processes.</p>