The study and measurement of optical nonlinearities, specifically the nonlinear refractive index and absorption coefficient, are crucial for advancements in various applications ranging from spectroscopy and material processing to biophysics, atmospheric sensing, and metrology. These properties are pivotal for developing innovative technologies and for predicting material responses in targeted applications. For instance, materials with pronounced two-photon absorption are pivotal in microfabrication, optical data storage, bio-imaging, and optical power limiting, whereas those with a significant nonlinear refractive index find applications in optical switching and soliton generation. The Z-scan technique, renowned for its effectiveness in determining these nonlinear optical properties, will be central to our project. We aim to create a fully automated experimental setup capable of acquiring multiple Z-scan traces and images at varying laser intensities and apertures autonomously. Additionally, we will integrate machine learning algorithms to enhance the optimization and predictive analysis of a material's nonlinear optical properties. Our experimentation will involve the use of a femtosecond laser in the infrared spectrum, along with substances like ethanol, methanol, and carbon disulfide for calibration and validation of our methodology.