Step 1: Obtain the vehicle's owners' book.
Step 2: Find, within that book, the chart outlining the age and distance at which various checks are to be performed. If the vehicle had multiple choices of powertrains, ensure that you are following the chart for the correct one.
Step 3: Follow it.
If you are in an end-user position - Why is machine learning necessary for something like this? If you deviate from manufacturer's recommendations, particularly during the warranty period, and something unexpected happens, you may encounter difficulties obtaining warranty coverage. The only thing you need to "learn" is the manufacturer's recommended maintenance schedule.
If you are in an OEM position or perhaps a fleet operator - Many vehicles nowadays have somewhat-automated maintenance reminders. Are you trying to train something like that? Most of them just count off calendar time and distance. Some of them might also count off cold-start cycles or running time. A few of them may account for events such as distance or time above a particular RPM threshold, or above or below particular oil or coolant temperatures, indicative of severe-service operation (hard driving, trailer-tow, short-trip operation, operation in severe hot or cold weather). Time or distance driven in fault conditions ("check-engine" fault warning lamp on due to misfire or air-fuel-ratio abnormality) might be an interesting factor, too.
Some engines have timing belts needing periodic replacement. Petrol engines have sparkplugs needing periodic replacement. Air filters need periodic replacement but how is your fancy machine-learning system going to know if the car drives through a sandstorm, or a forest fire, or a volcanic eruption? Belts need periodic replacement. Coolant needs periodic replacement. Lots and lots of other stuff nowadays needs no scheduled maintenance but goes in the "fix when broken" category.
How is your machine-learning system going to know if the operator used the correct grade of fancy synthetic oil meeting a particular tight specification ... or the cheapest stuff they could find that said "oil" on the bottle?
Same, but substitute "coolant" for "oil"?
There's a lot of differences in both powertrains and applications that I have my doubts! What's appropriate for a smaller engine having 3 litres of oil in the sump might not be appropriate for something having 15 litres in the sump.
What are you going to use for feedback that your machine-learning has learned the correct things?