This work presents the design, development, and analysis of cross-capacitance-based planar sensors designed to detect moisture and 2-furaldehyde (2-FAL) concentrations in transformer oil, employing the Thompson-Lampard theorem. The proposed sensors utilize Polyvinyl Alcohol (PVA) for humidity sensing and Polydimethylsiloxane (PDMS) for 2-FAL detection, leveraging these materials’ distinct hydrophilic and hydrophobic properties. The ANSYS Maxwell simulation environment models and emulates the sensor's design. The results show that the capacitance of a fixed-length cross-capacitance structure depends only on the dielectric constant of the transformer oil and is not affected by the sensor's geometry. Experimental verification shows that thin film-based sensors have increased sensitivity in detecting moisture and 2-FAL levels in oil, ranging from 0 to 60 ppm. Finally, the selectivity of the thin-film based sensing is established for the simultaneous moisture and 2-FAL presence in transformer oil. This work offers a new, reliable, and selective method to detect moisture and 2-FAL concentrations in transformer oil with no effect of both on each other. It is a potential candidate for online condition monitoring, which could lower maintenance costs and increase power transformer reliability.