This definitely does not hold for all irrational or transcendental numbers. As noted in the comments by Fabian, various Liouville numbers come to mind as examples where the digits of the number are not at all uniformly distributed, yet these same numbers were constructed with the specific intention of being transcendental.
The property you refer to - this "equidistribution of digits" and such - is what defines the so-called simply normal numbers in base $10$. If you have heard $\pi$ exhibits this very property, it's technically wrong because so far $\pi$ has not been proven to be simply normal in any base. It is suspected to be simply normal in every base, and even (absolutely) normal in every base, but it remains an open problem.
Simple normality in base $b$ means that the frequency of each digit in the first $n$ digits tends to $1/b$ as $n$ tends to infinity. Normality in base $b$ means that for each finite digit sequence of length $k$, its frequency in the first $n$ digits tends to $1/b^k$ as $n$ tends to infinity.
In fact, it seems only rather contrived numbers, such as $0.123456789101112...$ (Champernowne's number, obtained by concatenation of the naturals) among others in the article, are known for sure to be simply normal in base $10$, and in fact is normal in base $10$ (but not even known to be simply normal in other bases that are not powers of $10$). Nothing is known about a lot of the more "natural" numbers - like $\pi,e,\sqrt 2$. But I suppose Chaitin's constant could be considered a "natural" number, and it is normal in every base.
Trivially, we do know that almost all real numbers are normal in every base, equivalently a real number drawn uniformly randomly from $[0,1]$ is normal with probability $1$.
As for how to prove it for common numbers? Well, the proof for Chaitin's constant (overview in this Math Overflow post) relies on its algorithmic randomness, which is in fact just a much stronger form of normality. Roughly, normality in base $b$ says that each finite digit sequence of length $k$ has frequency in the first $n$ digits tending to $b^{-k}$ as $n$ tends to infinity, whereas algorithmic randomness says that there is some constant $c$ such that for every $n$ the shortest program (in some prefix-free encoding) that outputs the first $n$ bits has bit-length at least $n-c$, which intuitively means incompressible up to a constant. Note that if a number was not normal, it can be compressed using arithmetic encoding. On the other hand, Champernowne's constant is a very clear example of a highly compressible (so not algorithmically random) but normal number, since the first $n$ bits can obviously be output by a fixed program run on $n$ (which can be stored in $O(\log n)$ bits in prefix-free encoding).
Since $\pi$'s digits do not follow some nice pattern like Champernowne's number, nor are they algorithmically random, it is unlikely that normality proofs for known normal numbers would give much clue for $\pi$. So far, at least.
Of course this raises the question of "why do we conjecture them to be normal then?" Empirical evidence based on the first trillions of digits of $\pi$ do 'support' it, but of course that is nowhere near proof. It is just like if you toss a coin $1000000$ times and observe $500469$ heads and $499531$ tails, and conclude that you do not have evidence that it is not a fair memoryless coin, since the number of heads for a fair memoryless coin would be in the range $[499500,500500]$ with likelihood about $1/2$. So does your observation count as empirical evidence that it is a fair memoryless coin? Not really... Lack of evidence against is not really evidence for. Similarly, that is all we have for the question of $\pi$'s normality, so far.
Also, such empirical evidence is notoriously hard to interpret. Again take the coin example, and suppose you observe exactly $500000$ heads and $500000$ tails. Would you think it is a memoryless coin? No! How about $500001$ heads and $499999$ tails?