Emergency medical services must decide where to put ambulances how many of each type to keep, which ambulance to send to which call and which road to choose in just a few minutes. Emergency medical services can benefit from operations research tools taught at university level and this paper shows how these tools can help Emergency medical services make all of these decisions in a single linked system. Using a sample data set for a made‑up city district with eight demand zones and five possible stations a linear programming problem fixes the mix of Advanced and Basic Life Support ambulances; a set‑covering integer program chooses stations that can reach every zone in eight minutes; a transportation model solved by Vogel’s Approximation Method and verified with the MODI method assigns trips from stations to demand clusters; the Hungarian method matches free ambulances to calls that happen at the same time; Dijkstra’s algorithm finds the quickest road path; and a nearest‑neighbour heuristic, a 2‑opt improvement and simulated annealing design a multi‑stop patient transport route. The linear programming solution gives 3 ALS and 8 BLS units that serve 82 trips each day; three of the five stations provide eight‑minute coverage; VAM achieves an optimal transportation cost of 377 minutes, which is 6.7 percent lower than the North‑West Corner starting point; the Hungarian assignment reduces total response time from 35 to 30 minutes, a 14.3 percent improvement compared with dispatching the nearest available ambulance; and both 2‑opt and simulated annealing cut the route time from 77.4 to 60.0 minutes, a 22.5 percent reduction that matches the exact optimum. The results show that simple spreadsheet‑ready operations research tools give measurable benefits, for Emergency medical services planning.