The availability, dependability, and security of their traditional product-focused enterprises must be taken care of while lowering their maintenance costs if manufacturers of production plants and machinery are to maintain their competitiveness. It has been challenging to reach the production equipment during operation and maintenance since it is scattered widely and in accessible but remote places. Artificial intelligence and the industrial internet of things (IoT) support continuous real-time monitoring of the machines to identify wear conditions early and schedule repairs in advance, reducing downtime, lowering maintenance costs, and boosting productivity. During metal removal process surface finish is the major requirement for lathe efficiency. To balance this activity addition of nanoparticles will provide surface finish for lathe tools and work piece. A deployable early warning system called a condition monitoring system uses sensor data to forecast failure and the remaining usable life of the system, assemblies, and its parts. The provision of a condition monitoring system for heavy-duty lathes in machine shops is proposed. This system would process data on the lubricant quality and temperature of the spindle bearings and gears as well as the distance between the tool post and the chuck from the machine parts. The node MCU versions have been thoroughly explored and analyzed, both conventionally and in a specially designed method enhanced using Arduino programming. The objective of this work is to provide remote monitoring and smart maintenance for lathe machine shop through IoT Blynk app, the operator receives messages and SMS about the behavior and performance of the lathe on a continuous basis once lathe is ON. This enables the operator to practice preventive maintenance and to recommend units for replacement as soon as necessary before any serious failure or interruption of the lathe machine happens.

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