This study is a systematic analysis of selected research articles about power-to-X (P2X)-related processes. The relevance of this resides in the fact that most of the world’s energy is produced using fossil fuels, which has led to a huge amount of greenhouse gas emissions that are the source of global warming. One of the most supported actions against such a phenomenon is to employ renewable energy resources, some of which are intermittent, such as solar and wind. This brings the need for large-scale, longer-period energy storage solutions. In this sense, the P2X process chain could play this role: renewable energy can be converted into storable hydrogen, chemicals, and fuels via electrolysis and subsequent synthesis with CO2. The main contribution of this study is to provide a systematic articulation of advanced data-driven methods and latest technologies such as the Internet of Things (IoT), big data analytics, and machine learning for the efficient operation of P2X-related processes. We summarize our findings into different working architectures and illustrate them with a numerical result that employs a machine learning model using historic data to define operational parameters for a given P2X process.