This study examined the influence of budget controls on financial stability in organisations, using Nansana Municipality as the case. Budget controls constitute the principal mechanism through which organisations align expenditure with resources, and their effectiveness determines whether an entity can meet obligations as they fall due, avoid the accumulation of arrears, and sustain operations without recourse to crisis measures. Nansana Municipality, one of Uganda's youngest and fastest-growing urban authorities located within the Greater Kampala Metropolitan Area, confronts an acute configuration of rapidly expanding service demand, a narrow and volatile local revenue base, and dependence on central transfers whose timing and magnitude it does not control, making it a demanding setting in which to examine whether budget controls can produce financial stability. The study adopted a positivist philosophy and a cross-sectional survey design, collecting data from 155 respondents drawn through stratified random sampling from a target population of 260 comprising municipal and division technical staff, finance and accounts personnel, internal audit, revenue collection officers, elected councillors, and heads of departments. A structured questionnaire measured four budget control dimensions, namely budget planning and preparation, budget implementation and expenditure control, budget monitoring and variance analysis, and revenue budgeting and collection control, against financial stability assessed through liquidity, arrears management, revenue predictability, and expenditure discipline. Analysis employed descriptive statistics, Pearson correlation, and multiple regression in SPSS version 26. Findings indicated that budget planning and preparation recorded the highest mean while revenue budgeting and collection control recorded the lowest. All dimensions correlated significantly and positively with financial stability, with budget implementation and expenditure control strongest (r = 0.701, p < 0.01), followed by budget monitoring and variance analysis (r = 0.669, p < 0.01), revenue budgeting and collection control (r = 0.612, p < 0.01), and budget planning and preparation (r = 0.534, p < 0.01). The regression model was significant (F = 44.91, p < 0.001), explaining 56.8 per