The ground beneath my feet trembled violently as I clung to a doorframe, watching in horror as buildings crumbled around me. This was the 2015 Gorkha earthquake in Nepal, a moment that would forever change my life and career path. As the dust settled and the true extent of the devastation became clear, I found my purpose: to address geotechnical challenges and build safer, more resilient infrastructure. Today, my fascination with machine learning (ML) technology stems from its transformative potential in civil engineering, especially in geotechnical applications. ML enables us to handle complex datasets, allowing for more efficient solutions to problems that were once laborious. For instance, landslide susceptibility mapping, which traditionally relied on deterministic models, can now be conducted using advanced algorithms like Random Forest and Support Vector Machines, significantly improving the accuracy of predictions. At the Geo-Infra Research Institute, I worked on a project developing a machine learning framework for slope stability prediction. We leveraged supervised learning techniques, including Decision Trees and Neural Networks, to assess the stability of slopes by integrating various geotechnical parameters such as soil properties, slope angles, and rainfall data. This work demonstrated how machine learning models could outperform traditional methods by identifying patterns and relationships in data that were previously overlooked. Currently, as a graduate research assistant at Jackson State University, I am further honing my skills in applying ML algorithms to geotechnical challenges. These techniques offer a more nuanced understanding of complex systems like soil classification, slope stability and liquefaction, helping mitigate risks from natural disasters. Machine learning has the potential to revolutionize civil engineering by offering more efficient, data-driven solutions to critical problems. By leveraging such technologies, I aim to contribute to a safer world where women in technology lead the way in tackling geotechnical and infrastructural challenges, ultimately improving communities and saving lives. As I reflect on that fateful day in Nepal, I'm inspired by how far we've come and excited about the future. The fusion of ML and geotechnical engineering isn't just a technological advancement; it's a beacon of hope for building more resilient societies. It's this potential for positive global impact that continues to drive my work, reminding me that with every line of code and every model we train, we're one step closer to a world where earthquakes like the one I experienced become less devastating.