Optimizing memory usage in Python applications involves utilizing efficient data types like `deque` for large datasets, employing generators to minimize memory overhead, and leveraging `numpy` for compact numerical computations. Tools like `memory_profiler` assist in tracking usage, while `pandas` streamlines data manipulation. Techniques like `weakref` and `__slots__` further enhance memory management.