This work evaluates various control algorithms in hydroponics farming, to analyse the performance of the algorithms with the aim to design an intelligent controller which can improve resource efficiency and crop yield by leveraging their ability to handle non linear systems more effectively than conventional controllers. The focus is on the nutrient solution section of the hydroponics farm, specifically monitoring and controlling pH, water level, and water temperature. To achieve this, both simulation and prototyping approaches are adopted. First, mathematical models for each environmental parameter are developed using first-order differential equations in the MATLAB Simulink platform. Conventional PID control, as well as intelligent controllers such as PID-Fuzzy Logic Control (PID-FLC), Fuzzy Logic Control (FLC), are designed and applied to the developed transfer functions. The performance of these algorithms are then evaluated using transient performance parameters: steady- state error, overshoot, settling time, and rise time. These metrics are used to compare the control systems against practical requirements set by farmers or users. Based on a systematic approach, the best-performing algorithm is selected for further evaluation in a prototype system together with a data driven control algorithm . The prototype consists of a 24-plant nutrient film technique (NFT) hydroponic system. Data collected from this setup are used to implement a data-driven Long Short Term Memory-Artificial Neural Network (LSTM-ANN) controller, which is then compared against the selected control algorithm from the simulation stage. The research showed that PID-FLC performed better in terms of responsiveness recording lower rise times and setting times. Specifically, the PID-FLC recorde rise times of 60 seconds for pH increase and 70 seconds for pH decrease, compared to the ANN’s 230 seconds and 120 seconds, respectively. For settling times pH up for PID-FLC 119.38 seconds ANN 320 seconds. In terms of steady-state error, all algorithms showed capabilities in maintaining required levels as they recorded low values close to 0 or range close to the setpoint. As for overshoot, the controllers were able to maintain overshoots of less than 2.5%. The study further analyses the financial viability of the use of an intelligent controller analysing the cost savings, net present value, and payback period of the project.