Production compilers have achieved a high level of maturity in terms of generating efficient code. The code generated by any two production compilers can turn out to be very different based on pros and cons of their respective Intermediate Representation (IR), implemented loop transformations and their ordering, cost models used and even instruction selection (such as vector instructions) and scheduling. Hence, the performance of produced code for a program segment by a given compiler may not necessarily be matched by other compilers. This paper proposes a meta-compilation framework, the MCompiler, which allows different segments of a program to be compiled with different compilers/optimizers and combined into a single executable. It turns the differences between compilation processes and performance optimizations in each compiler from a weakness to a strength. Utilizing the highest performing code for each segment can lead to a significant overall improvement in performance. A loop nest is used as a segment in this work, but other choices can be made. The question is, though, which compiler will produce the best code for a segment. This work then presents a technique to accomplish this using Machine Learning. It learns inherent characteristics of loop nests and then predicts during compilation which code optimizer is the most suited for each loop nest in an application. The results show that our framework improves the overall performance for applications over state-of-the-art compilers (compiled at equivalent of-O3) by a geometric mean of 1.97x for auto-vectorized code and 2.62x for auto-parallelized code. Parallel applications with OpenMP directives are also improved by the MCompiler, with a geometric mean performance improvement of 1.13x. The use of Machine Learning prediction achieves performance very close to the exploratory search for choosing the most suited code optimizer: within 4% for auto-vectorized code and within 8% for auto-parallelized code.