Because of the increase in hip implant operations over the last few decades, the design and manufacturing techniques have also been improving in order to create the optimal design that suits each individual patient and minimizes the need for revisional surgery. The hip implant fixture into the human bones has been the main concern from the biomechanical point of view. Lattice topology optimization has aided in supporting the osseointegration properties since the lattice structures have shown, once applied, the ability to produce a close-to-bone structure due to their properties. Due to their properties, lattice structures have aided in supporting the osseointegration properties once applied to the implant, leading to a better fixture for implants as strange objects into the human bone. However, the properties of the lattice structures change based on their parameters, which in turn can complicate the structure's shape and topology, making the manufacturing process a complicated task. For the purpose of saving the time and effort of the implant creation process, machine learning methods have come to support the prediction of the mechanical properties, and additive manufacturing techniques have handled the manufacturing of the complex topology of the implant structure. This doctoral dissertation provides a comprehensive and carefully updated examination of literature pertaining to the process of the lattice optimization of hip implants. Additionally, it goes deep into the complex field of practical design optimization for the hip implant using lattice structures. With the aid of machine learning techniques, these lattice structure properties have been calculated according to desired mechanical properties. Thus, the process ensures higher overall efficiency and precision of the implant design. The research investigates material selection, where the study shows the benefits of using the titanium Ti6Al4V alloy with the lattice structures design due to its suitable properties. This alloy has a high corrosion resistance and almost no critical interaction with the human body fluid. The alloy’s Young’s modulus is also calculated to assess the elasticity of the selected material. The dissertation also focuses on the initial computer-aided design of the lattice structures and their applied types. Three distinct types of latticed structures with different shapes are discussed. The mechanical properties relating to unit cells and the effect they have on the general implant design are investigated. Using one of the machine learning techniques, Linear regression, the porosity of the latticed design is pre-set to be in a suitable range that results in better topology and mechanical properties for the implant design. Total numerical simulation is performed on the three designs of unit cells, then the specimens are 3D-printed, and a laboratory compression test is performed to validate the results of the simulations.