Need additional clarification on multi linear regression

is a multiple linear regression combination of several linear regressions ?

For example take this

Simple linear regression:
model 1 : ‘salary = age’
parameters : slope1, intercept1

model 2 : ‘salary = experience’
parameters : slope 2, intercept 2

Multiple linear regression
model 3 : ‘salary = age + experience’
parameters : slope_age, slope_experience, intercept 3

As far as i know its not a combination of several linear regressions because
slope_age ≠ slope1,
similarly slope_experience ≠ slope2

why this happens, isnt multilinear regression a combination of several linear regressions, what makes the difference ?

hoping for help!!!

No this is a linear with multiple input features. You may even have polynomials with many input features that still fall under linear regression.

“linear regression” does not mean you have only one feature and the hypothesis must be a straight line in 2D.

You can have multiple features, their linear combination (the sum of the weighted features) is the hypothesis.