Condition monitoring of machinery through the analysis of lubricating oil is crucial for preventing catastrophic failures and ensuring optimal performance. This study describes two innovative sensor concepts for online monitoring of lubrication oil properties: an inductive-based sensor for wear debris detection and a capacitance sensor for pH evaluation. The inductive-based wear debris sensor offers a cost-effective solution for detecting ferrous and nonferrous particles in the oil. During operation, friction generates wear debris that circulates in the oil system. Ferrous particles increase sensing coil inductance, while nonferrous particles decrease it. A high-resolution inductance-to-digital converter precisely measures these changes in coil inductance based on shifts in resonance frequency. Experimental studies with debris sizes ranging from 40 to 300 microns demonstrate the sensor's effectiveness in static and dynamic conditions for ferrous and nonferrous particles.On the other hand, the capacitance sensor aims to assess the pH of oil by correlating it with the oil's dielectric properties. As the pH of lubricating oil shifts due to additive degradation over time, the dielectric properties change accordingly. Two different methods for measurement are explored, one is using a high-resolution capacitance to digital converter and other using an impedance analyser. The prototype is tested with different oil grades, each with varying pH levels. Combining these sensors provides a comprehensive approach to machine health monitoring. This integrated system offers remote, low-cost monitoring with the potential for proactive maintenance, enhancing machinery reliability and lifespan.