Why don’t we calculate the average of an entire given population instead of computing confidence interval to estimate the population mean? We compute confidence intervals to estimate the true population mean of either a sample (when population standard deviation is unknown) or an entire population (when population standard deviation is known). But I wonder why, if we are given an entire population, why don't we calculate the average of that population instead of computing confidence interval to estimate the population mean since we already have the entire population? I'm not a Math or Stat major so I apologize if my question does not sound "smart".

Maybe I am just imagining this, but from what you
say in your question, it seems you might be receptive to the idea of bootstrapping, briefly
illustrated below.
Suppose you have a random sample of size $50$ from some unknown
population. You don't know the population mean or standard deviation, nor do you know the general shape of the distribution. You do assume the population mean $\mu$ exists and want a 95% confidence interval of $\mu.$ A boxplot of the
fifty observations at hand is as shown.
boxplot(x, horizontal=T, col="skyblue2", pch=20)


If you had a good idea of the variability of the
sample means $\bar X$ around the population $\mu$ mean, you could use some
relationship such as $D = \bar X -\mu$ then you might use something like $P(L_D < \bar X-\mu < U_D) = 0.95$ to get the 95% CI $\bar X -U_D, \bar X-L_D.$
Instead, you look at many 're-samples' of 50 observations, sampling with replacement from the
data you have. A typical number of re-samples is $B = 2000,$ a job for a computer.) From them you can often get an idea
of the values of $U_D$ and $L_D,$ and hence an
approximate CI for $\mu.$ One 95% bootstrap CI for $\mu$ turns out to be $(22.0,\, 28.3).$
a.obs = mean(x);  a.obs
[1] 25.28573
set.seed(626)
d = replicate(2000, mean(sample(x, 50, rep=T))-a.obs)
UL = quantile(d, c(.975,.025))
a.obs - UL
  97.5%     2.5% 
22.04661 28.26154     ## Bootstrap CI.

Of course, the randomness in the 'resampling' will
lead to a slightly different bootstrap CI on each run. (My partial cure for that is heavy rounding of endpoints.) Also, there are many different styles of bootstrap CIs.
Note: The data in the example above are sampled in R from a gamma distribution with $\mu = 25.$
set.seed(2022)
x = rgamma(50, 4, 4/25) 

If you don't like the bootstrapping idea, then I recommend @Dave's (+1) Answer.
