Comparing two strings
Algorithms for computing the Levenshtein edit distance between a pair of strings can be found in the Wikipedia page on the edit distance. As that page explains, the running time for computing the edit distance between two strings of length $M$ is $O(M^2)$ time and $O(M)$ space. If you only care about whether the edit distance is $\le T$ or $>T$, then the running time can be reduced to $O(MT)$ time and $O(M)$ space.
Algorithms for computing the (full) Damerau-Levenshtein distance are linked to on the corresponding Wikipedia page. As Wikipedia explains, the (full) Damerau-Levenshtein distance can be computed in $O(M^2)$ time and $O(M^2)$ space; the algorithm is found in the Lowrance and Wagner paper cited there, or in Appendix A of the Bard paper cited there.
Wikipedia also shows how to compute what is known as restricted Damerau-Levenshtein edit distance; that is considerably easier than computing the full Damerau-Levenshtein distance, and the restricted Damerau-Levenshtein edit distance can be computed using a simple modification to the standard dynamic programming algorithm for the Levenshtein edit distance. The running time is $O(M^2)$, and it can be reduced to $O(MT)$ time if you only care about whether the distance is $\le T$ or $>T$.
Wikipedia also claims that for spelling correction and typo detection in natural language, the restricted Damerau-Levenshtein edit distance rarely differs from the full Damerau-Levenshtein distance, so you might as well use the restricted distance as it is easier to understand and implement. I have no personal knowledge of this; just passing along what is claimed in Wikipedia.
I recommend reading Appendix A of the Bard paper cited at Wikipedia for further implementation details.
Comparing all pairs: naive computation of pairwise distances
Therefore, the naive algorithm of computing the edit distance between each pair of strings will take $O(N^2 MT)$ time and $O(M)$ space.
However, for real-world problems of the sort you mentioned, this naive algorithm is typically not optimal. If $T$ is fairly small, there are much better algorithms; the best algorithm depends heavily on $T$. But since you haven't specified $T$ in your question, you haven't provided us enough information to suggest a specific algorithm.
Comparing all pairs: better data structures
A related problem is: given the set of $N$ strings, compute a data structure that allows us to quickly find the closest one in the set to any given string (under edit distance as our measure of "closeness"). There is lots of theoretical work on this problem, e.g., using BK-trees, Levenshtein automata, and many other techniques. See especially Efficient map data structure supporting approximate lookup and https://cstheory.stackexchange.com/q/4165/5038 for an entry into the literature.
In your case, this might be overkill, though.
The case where $T=1$: sorting
In the special case where $T=1$, there is a clean algorithm. In other words, we want to consider two strings $X,Y$ if their edit distance is $1$ (i.e., $X$ can be transformed into $Y$ with a single insertion, deletion, or substitution, transposition).
Notice that if $d(X,Y)=1$, then the single difference must appear in either the first half of the string or in the last half of the string. In other words, either the first half of $X$ matches the first half of $Y$ (they have a long matching prefix), or the second half of $X$ matches the second half of $Y$ (they have a long matching suffix).
This immediately suggests a nice two-stage algorithm:
Sort the entire list of strings. For each string $X$, enumerate all other strings $Y$ that agree with $Y$ in its first half and check whether any of them are at edit distance $1$ from $X$. This can be done through a linear scan of a short contiguous stretch of the sorted list, and the start and end of that stretch can be found quickly with binary search.
Next, reverse all the strings and do the same thing again.
(Note that you need to be a little bit careful about the definition of "half" above, to take into account that $X,Y$ might differ in their length. However these pesky details can be easily worked out with a little bit of care.)
The space requirement is $O(N)$. Sorting takes $O(N \lg N)$ time. If you're lucky and the strings are fairly random, then hopefully each string will have only a small number of other candidates that match in the first half or last half. If this is the case, the running time might be in the vicinity $O(N \lg N)$.
Does this generalize to $T>1$? Maybe, but it gets messier.
One way to try to generalize is to split the string into $T+1$ equal-length segments, and note that if $d(X,Y)\le T$, then there must be some segment where they match. However, this runs into messy details because the length of $X$ might differ from the length of $Y$. Therefore, if $T>1$, I don't recommend this approach.
Another way to try to generalize is to break each string down into different fields: say the street address, the city, the country, the postal code. Now if two addresses $X,Y$ have edit distance $\le 2$, then either (a) some field of $X$ is at edit distance $2$ from $Y$, and all other fields match exactly, or (b) there are two fields where they have edit distance $1$, and all other fields match exactly. You can find all matches of type (a) by, for each field, building a hashtable keyed on everything but that field; now any two strings that match will be mapped to the same hash bucket, so you just need to solve the problem separately for each bucket. You can also find all matches of type (b) similarly. This might be more promising, depending upon the structure of your problem.
In practice, there are some techniques that I suspect will probably be helpful. One is to try to use spell-correction on each field of the address first, and then use the spell-corrected addresses to look for duplicates (confirming them by reference to the original value).
Another possible heuristic might be to map each address to geographical coordinates on the sphere, and look for pairs of addresses that are locally very near each other. This won't detect typos in (say) the country or state, but it might be helpful in detecting typos in (say) street address, by reducing the set of pairwise comparisons you need to make.
You might also like to look into the literature on how to build fast spell checkers, as that is a closely related problem. Take a look at Peter Norvig's article on that problem: http://norvig.com/spell-correct.html