We examine the possibility of using neural networks and machine learning to investigate the string landscape. Since the topic is rather new, we will start with an introduction to machine learning and review possible applications for the landscape. Given the versatile nature and convoluted structure of the landscape, we propose to dynamically evolve the neural networks that feature in the analysis via genetic algorithms. This means that we start from basic building blocks and combine them such that the neural network performs best for the application we are interested in. In the end, we will look at concrete examples that illustrate how neural networks can be used to study stability and the chiral spectrum of vector bundles in heterotic theories.